Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles

ABSTRACT

A method provides an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject. An embodiment of this method includes: deriving from the sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables evaluation of the biological condition; and in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable; and applying values from the profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the subject.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.11/158,504, filed Jun. 22, 2005, which is a continuation of U.S.application Ser. No. 10/291,856, filed Nov. 8, 2002, which in turnclaims priority to U.S. Application Ser. No. 60/348,213, filed Nov. 9,2001, U.S. Application Ser. No. 60/340,881, filed Dec. 7, 2001, U.S.Application Ser. No. 60/369,633, filed Apr. 3, 2002, and U.S.Application Ser. No. 60/376,997, filed Apr. 30, 2002. The contents ofeach of these applications are hereby incorporated by reference in theirentireties.

TECHNICAL FIELD AND BACKGROUND ART

The present invention relates to use of gene expression data, and inparticular to use of gene expression data in identification, monitoringand treatment of disease and in characterization of biological conditionof a subject.

The prior art has utilized gene expression data to determine thepresence or absence of particular markers as diagnostic of a particularcondition, and in some circumstances have described the cumulativeaddition of scores for over expression of particular disease markers toachieve increased accuracy or sensitivity of diagnosis. Information onany condition of a particular patient and a patient's response to typesand dosages of therapeutic or nutritional agents has become an importantissue in clinical medicine today not only from the aspect of efficiencyof medical practice for the health care industry but for improvedoutcomes and benefits for the patients.

SUMMARY OF THE INVENTION

In a first embodiment, there is provided a method, for evaluating abiological condition of a subject, based on a sample from the subject.The method includes: deriving from the sample a profile data set, theprofile data set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables evaluation of the biological condition; and

in deriving the profile data set, achieving such measure for eachconstituent under measurement conditions that are substantiallyrepeatable.

There is a related embodiment for providing an index that is indicativeof the state of a subject, as to a biological condition, based on asample from the subject. This embodiment includes:

deriving from the sample a profile data set, the profile data setincluding a plurality of members, each member being a quantitativemeasure of the amount of a distinct RNA or protein constituent in apanel of constituents selected so that measurement of the constituentsenables evaluation of the biological condition; and

in deriving the profile data set, achieving such measure for eachconstituent under measurement conditions that are substantiallyrepeatable; and

applying values from the profile data set to an index function thatprovides a mapping from an instance of a profile data set into asingle-valued measure of biological condition, so as to produce an indexpertinent to the biological condition of the subject.

In further embodiments related to the foregoing, there is also included,in deriving the profile data set, achieving such measure for eachconstituent under measurement conditions wherein specificity andefficiencies of amplification for all constituents are substantiallysimilar. Similarly further embodiments include alternatively or inaddition, in deriving the profile data set, achieving such measure foreach constituent under measurement conditions wherein specificity andefficiencies of amplification for all constituents are substantiallysimilar.

In embodiments relating to providing the index a further embodiment alsoincludes providing with the index a normative value of the indexfunction, determined with respect to a relevant population, so that theindex may be interpreted in relation to the normative value. Optionallyproviding the normative value includes constructing the index functionso that the normative value is approximately 1. Also optionally, therelevant population has in common a property that is at least one of agegroup, gender, ethnicity, geographic location, diet, medical disorder,clinical indicator, medication, physical activity, body mass, andenvironmental exposure.

In another related embodiment, efficiencies of amplification, expressedas a percent, for all constituents lie within a range of approximately 2percent, and optionally, approximately 1 percent.

In another related embodiment, measurement conditions are repeatable sothat such measure for each constituent has a coefficient of variation,on repeated derivation of such measure from the sample, that is lessthan approximately 3 percent.

In further embodiments, the panel includes at least three constituentsand optionally fewer than approximately 500 constituents.

In another embodiment, the biological condition being evaluated is withrespect to a localized tissue of the subject and the sample is derivedfrom tissue or fluid of a type distinct from that of the localizedtissue.

In related embodiments, the biological condition may be any of theconditions identified in Tables 1 through 12 herein, in which case thereare measurements conducted corresponding to constituents of thecorresponding Gene Expression Panel. The panel in each case includes atleast two, and optionally at least three, four, five, six, seven, eight,nine or ten, of the constituents of the corresponding Gene ExpressionPanel.

In another embodiment, there is provided a method of providing an indexthat is indicative of the inflammatory state of a subject based on asample from the subject that includes: deriving from the sample a firstprofile data set, the first profile data set including a plurality ofmembers, each member being a quantitative measure of the amount of adistinct RNA or protein constituent in a panel of constituents, thepanel including at least two of the constituents of the InflammationGene Expression Panel of Table 1; (although in other embodiments, atleast three, four, five, six or ten constituents of the panel of Table 1may be used in a panel) wherein, in deriving the first profile data set,such measure is performed for each constituent both under conditionswherein specificity and efficiencies of amplification for allconstituents are substantially similar and under substantiallyrepeatable conditions; and applying values from the first profile dataset to an index function that provides a mapping from an instance of aprofile data set into a single-valued measure of biological condition(in an embodiment, this may be an inflammatory condition), so as toproduce an index pertinent to the biological condition of the sample orthe subject. The biological condition may be any condition that isassessable using an appropriate Gene Expression Panel; the measurementof the extent of inflammation using the Inflammation Gene ExpressionPanel is merely an example.

In additional embodiments, the mapping by the index function may befurther based on an instance of a relevant baseline profile data set andvalues may be applied from a corresponding baseline profile data setfrom the same subject or from a population of subjects or samples with asimilar or different biological condition. Additionally, the indexfunction may be constructed to deviate from a normative value generallyupwardly in an instance of an increase in expression of a constituentwhose increase is associated with an increase of inflammation and alsoin an instance of a decrease in expression of a constituent whosedecrease is associated with an increase of inflammation. The indexfunction alternatively be constructed to weigh the expression value of aconstituent in the panel generally in accordance with the extent towhich its expression level is determined to be correlated with extent ofinflammation. The index function may be alternatively constructed totake into account clinical insight into inflammation biology or to takeinto account experimentally derived data or to take into accountrelationships derived from computer analysis of profile data sets in adata base associating profile data sets with clinical and demographicdata. In this connection, the construction of the index function may beachieved using statistical methods, which evaluate such data, toestablish a model of constituent expression values that is an optimizedpredictor of extent of inflammation.

In another embodiment, the panel includes at least one constituent thatis associated with a specific inflammatory disease.

The methods described above may further utilize the step wherein (i) themapping by the index function is also based on an instance of at leastone of demographic data and clinical data and (ii) values are appliedfrom the first profile data set including applying a set of valuesassociated with at least one of demographic data and clinical data.

In another embodiment of the above methods, a portion of deriving thefirst profile data set is performed at a first location and applying thevalues from the first profile data set is performed at a secondlocation, and data associated with performing the portion of derivingthe first profile data set are communicated to the second location overa network to enable, at the second location, applying the values fromthe first profile data set.

In an embodiment of the methods, the index function is a linear sum ofterms, each term being a contribution function of a member of theprofile data set. Moreover, the contribution function may be a weightedsum of powers of one of the member or its reciprocal, and the powers maybe integral, so that the contribution function is a polynomial of one ofthe member or its reciprocal. Optionally, the polynomial is a linearpolynomial. The profile data set may include at least three, four or allmembers corresponding to constituents selected from the group consistingof IL1A, IL1B, TNF, IFNG and IL10. The index function may beproportional to ¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1{IL10} and braces arounda constituent designate measurement of such constituent.

In an additional embodiment, a method is provided of analyzing complexdata associated with a sample from a subject for information pertinentto inflammation, the method that includes: deriving a Gene ExpressionProfile for the sample, the Gene Expression Profile being based on aSignature Panel for Inflammation; and using the Gene Expression Profileto determine a Gene Expression Profile Inflammatory Index for thesample.

In an additional embodiment, a method is provided of monitoring thebiological condition of a subject, that includes deriving a GeneExpression Profile for each of a series of samples over time from thesubject, the Gene Expression Profile being based on a Signature Panelfor Inflammation; and for each of the series of samples, using thecorresponding Gene Expression Profile to determine a Gene ExpressionProfile Inflammatory Index.

In an additional embodiment, there is provided a method of determiningat least one of (i) an effective dose of an agent to be administered toa subject and (ii) a schedule for administration of an agent to asubject, the method including: deriving a Gene Expression Profile for asample from the subject, the Gene Expression Profile being based on aSignature Panel for Inflammation; using the Gene Expression Profile todetermine a Gene Expression Profile Inflammatory Index for the sample;and

using the Gene Expression Profile Inflammatory Index as an indicator inestablishing at least one of the effective dose and the schedule.

In an additional embodiment, a method of guiding a decision to continueor modify therapy for a biological condition of a subject, is providedthat includes: deriving a Gene Expression Profile for a sample from thesubject, the Gene Expression Profile being based on a Signature Panelfor Inflammation; and using the Gene Expression Profile to determine aGene Expression Profile Inflammatory Index for the sample.

A method of predicting change in biological condition of a subject as aresult of exposure to an agent, is provided that includes: deriving afirst Gene Expression Profile for a first sample from the subject in theabsence of the agent, the first Gene Expression Profile being based on aSignature Panel for Inflammation; deriving a second Gene ExpressionProfile for a second sample from the subject in the presence of theagent, the second Gene Expression Profile being based on the sameSignature Panel; and using the first and second Gene Expression Profilesto determine correspondingly a first Gene Expression ProfileInflammatory Index and a second Gene Expression Profile InflammatoryIndex. Accordingly, the agent may be a compound and the compound may betherapeutic.

In an additional embodiment, a method of evaluating a property of anagent is provided where the property is at least one of purity, potency,quality, efficacy or safety, the method including: deriving a first GeneExpression Profile from a sample reflecting exposure to the agent of (i)the sample, or (ii) a population of cells from which the sample isderived, or (iii) a subject from which the sample is derived; using theGene Expression Profile to determine a Gene Expression ProfileInflammatory Index; and using the Gene Expression Profile InflammatoryIndex in determining the property.

In accordance with another embodiment there is provided a method ofproviding an index that is indicative of the biological state of asubject based on a sample from the subject. The method of thisembodiment includes:

deriving from the sample a first profile data set, the first profiledata set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents, the panel including at least twoof the constituents of the Inflammation Gene Expression Panel of Table1; and

applying values from the first profile data set to an index functionthat provides a mapping from an instance of a profile data set into asingle-valued measure of biological condition, so as to produce an indexpertinent to the biological condition of the sample or the subject.

In carrying out this method the index function also uses data from abaseline profile data set for the panel. Each member of the baselinedata set is a normative measure, determined with respect to a relevantpopulation of subjects, of the amount of one of the constituents in thepanel. In addition, in deriving the first profile data set and thebaseline data set, such measure is performed for each constituent bothunder conditions wherein specificity and efficiencies of amplificationfor all constituents are substantially similar and under substantiallyrepeatable conditions.

In another type of embodiment, there is provided a method, forevaluating a biological condition of a subject, based on a sample fromthe subject. In this embodiment, the method includes:

deriving from the sample a first profile data set, the first profiledataset including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein eachmember of the calibrated profile data set is a function of acorresponding member of the first profile data set and a correspondingmember of a baseline profile data set for the panel.

In this embodiment, each member of the baseline data set is a normativemeasure, determined with respect to a relevant population of subjects,of the amount of one of the constituents in the panel, and thecalibrated profile data set provides a measure of the biologicalcondition of the subject.

In a similar type of embodiment, there is provided a method, forevaluating a biological condition of a subject, based on a sample fromthe subject, and the method of this embodiment includes:

applying the first sample or a portion thereof to a defined populationof indicator cells;

obtaining from the indicator cells a second sample containing at leastone of RNAs or proteins;

deriving from the second sample a first profile data set, the firstprofile data set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein eachmember of the calibrated profile data set is a function of acorresponding member of the first profile data set and a correspondingmember of a baseline profile data set for the panel, wherein each memberof the baseline data set is a normative measure, determined with respectto a relevant population of subjects, of the amount of one of theconstituents in the panel, the calibrated profile data set providing ameasure of the biological condition of the subject.

Furthermore, another and similar, type of embodiment provides a method,for evaluating a biological condition affected by an agent. The methodof this embodiment includes:

obtaining, from a target population of cells to which the agent has beenadministered, a sample having at least one of RNAs and proteins;

deriving from the sample a first profile data set, the first profiledata set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein eachmember of the calibrated profile data set is a function of acorresponding member of the first profile data set and a correspondingmember of a baseline profile data set for the panel, wherein each memberof the baseline data set is a normative measure, determined with respectto a relevant population of subjects, of the amount of one of theconstituents in the panel, the calibrated profile data set providing ameasure of the biological condition as affected by the agent.

In further embodiments based on these last three embodiments, therelevant population may be a population of healthy subjects.Alternatively, or in addition, the relevant population is has in commona property that is at least one of age group, gender, ethnicity,geographic location, diet, medical disorder, clinical indicator,medication, physical activity, body mass, and environmental exposure.

Alternatively or in addition, the panel includes at least two of theconstituents of the Inflammation Gene Expression Panel of Table 1.(Other embodiments employ at least three, four, five, six, or ten ofsuch constituents.) Also alternatively or in addition, in deriving thefirst profile data set, such measure is performed for each constituentboth under conditions wherein specificity and efficiencies ofamplification for all constituents are substantially similar and undersubstantially repeatable conditions. Also alternatively, when suchmeasure is performed for each constituent both under conditions whereinspecificity and efficiencies of amplification for all constituents aresubstantially similar and under substantially repeatable conditions,optionally one need not produce a calibrated profile data set, but mayinstead work directly with the first data set.

In another embodiment, there is provided a method, for evaluating theeffect on a biological condition by a first agent in relation to theeffect by a second agent. The method of this embodiment includes:

obtaining, from first and second target populations of cells to whichthe first and second agents have been respectively administered, firstand second samples respectively, each sample having at least one of RNAsand proteins;

deriving from the first sample a first profile data set and from thesecond sample a second profile data set, the profile data sets eachincluding a plurality of members, each member being a quantitativemeasure of the amount of a distinct RNA or protein constituent in apanel of constituents selected so that measurement of the constituentsenables measurement of the biological condition; and

producing for the panel a first calibrated profile data set and a secondprofile data set, wherein (i) each member of the first calibratedprofile data set is a function of a corresponding member of the firstprofile data set and a corresponding member of a baseline profile dataset for the panel, wherein each member of the baseline data set is anormative measure, determined with respect to a relevant population ofsubjects, of the amount of one of the constituents in the panel, and(ii) each member of the second calibrated profile data set is a functionof a corresponding member of the second profile data set and acorresponding member of the baseline profile data set, the calibratedprofile data sets providing a measure of the effect by the first agenton the biological condition in relation to the effect by the secondagent.

In this embodiment, in deriving the first and second profile data sets,such measure is performed for each constituent both under conditionswherein specificity and efficiencies of amplification for allconstituents are substantially similar and under substantiallyrepeatable conditions. In a further related embodiment, the first agentis a first drug and the second agent is a second drug. In anotherrelated embodiment, the first agent is a drug and the second agent is acomplex mixture. In yet another related embodiment, the first agent is adrug and the second agent is a nutriceutical.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the invention will be more readily understoodby reference to the following detailed description, taken with referenceto the accompanying drawings, in which:

FIG. 1A shows the results of assaying 24 genes from the SourceInflammation Gene Panel (shown in Table 1) on eight separate days duringthe course of optic neuritis in a single male subject.

1B illustrates use of an inflammation index in relation to the data ofFIG. 1A, in accordance with an embodiment of the present invention.

FIG. 2 is a graphical illustration of the same inflammation indexcalculated at 9 different, significant clinical milestones.

FIG. 3 shows the effects of single dose treatment with 800 mg ofibuprofen in a single donor as characterized by the index.

FIG. 4 shows the calculated acute inflammation index displayedgraphically for five different conditions.

FIG. 5 shows a Viral Response Index for monitoring the progress of anupper respiratory infection (URI).

FIGS. 6 and 7 compare two different populations using Gene ExpressionProfiles (with respect to the 48 loci of the Inflammation GeneExpression Panel of Table 1).

FIG. 8 compares a normal population with a rheumatoid arthritispopulation derived from a longitudinal study.

FIG. 9 compares two normal populations, one longitudinal and the othercross sectional.

FIG. 10 shows the shows gene expression values for various individualsof a normal population.

FIG. 11 shows the expression levels for each of four genes (of theInflammation Gene Expression Panel of Table 1), of a single subject,assayed monthly over a period of eight months.

FIGS. 12 and 13 similarly show in each case the expression levels foreach of 48 genes (of the Inflammation Gene Expression Panel of Table 1),of distinct single subjects (selected in each case on the basis offeeling well and not taking drugs), assayed, in the case of FIG. 12weekly over a period of four weeks, and in the case of FIG. 13 monthlyover a period of six months.

FIG. 14 shows the effect over time, on inflammatory gene expression in asingle human subject, of the administration of an anti-inflammatorysteroid, as assayed using the Inflammation Gene Expression Panel ofTable 1.

FIG. 15, in a manner analogous to FIG. 14, shows the effect over time,via whole blood samples obtained from a human subject, administered asingle dose of prednisone, on expression of 5 genes (of the InflammationGene Expression Panel of Table 1).

FIG. 16 also shows the effect over time, on inflammatory gene expressionin a single human subject suffering from rheumatoid arthritis, of theadministration of a TNF-inhibiting compound, but here the expression isshown in comparison to the cognate locus average previously determined(in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed,healthy) population.

FIG. 17A further illustrates the consistency of inflammatory geneexpression in a population.

FIG. 17B shows the normal distribution of index values obtained from anundiagnosed population.

FIG. 17C illustrates the use of the same index as FIG. 17B, where theinflammation median for a normal population has been set to zero andboth normal and diseased subjects are plotted in standard deviationunits relative to that median.

FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene ExpressionProfiles, for the same 7 loci as in FIG. 17A, two different (responderv. non-responder) 6-subject populations of rheumatoid arthritispatients.

FIG. 19 thus illustrates use of the inflammation index for assessment ofa single subject suffering from rheumatoid arthritis, who has notresponded well to traditional therapy with methotrexate.

FIG. 20 similarly illustrates use of the inflammation index forassessment of three subjects suffering from rheumatoid arthritis, whohave not responded well to traditional therapy with methotrexate.

Each of FIGS. 21-23 shows the inflammation index for an internationalgroup of subjects, suffering from rheumatoid arthritis, undergoing threeseparate treatment regimens.

FIG. 24 illustrates use of the inflammation index for assessment of asingle subject suffering from inflammatory bowel disease.

FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of theInflammation Gene Expression Panel of Table 1) for whole blood treatedwith Ibuprofen in vitro in relation to other non-steroidalanti-inflammatory drugs (NSAIDs).

FIG. 26 illustrates how the effects of two competing anti-inflammatorycompounds can be compared objectively, quantitatively, precisely, andreproducibly.

FIGS. 27 through 41 illustrate the use of gene expression panels inearly identification and monitoring of infectious disease.

FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes,developed to discriminate various bacterial conditions in a hostbiological system.

FIG. 28 shows differential expression for a single locus, IFNG, to LTAderived from three distinct sources: S. pyogenes, B. subtilis, and S.aureus.

FIGS. 29 and 30 show the response after two hours of the Inflammation48A and 48B loci respectively (discussed above in connection with FIGS.6 and 7 respectively) in whole blood to administration of aGram-positive and a Gram-negative organism.

FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and aresimilar to them, with the exception that the monitoring here occurs 6hours after administration.

FIG. 33 compares the gene expression response induced by E. coli and byan organism-free E. coli filtrate.

FIG. 34 is similar to FIG. 33, but here the compared responses are tostimuli from E. coli filtrate alone and from E. coli filtrate to whichhas been added polymyxin B.

FIG. 35 illustrates the gene expression responses induced by S. aureusat 2, 6, and 24 hours after administration.

FIGS. 36 through 41 compare the gene expression induced by E. coli andS. aureus under various concentrations and times.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS Definitions

The following terms shall have the meanings indicated unless the contextotherwise requires:

“Algorithm” is a set of rules for describing a biological condition. Therule set may be defined exclusively algebraically but may also includealternative or multiple decision points requiring domain-specificknowledge, expert interpretation or other clinical indicators.

An “agent” is a“composition” or a “stimulus”, as those terms are definedherein, or a combination of a composition and a stimulus.

“Amplification” in the context of a quantitative RT-PCR assay is afunction of the number of DNA replications that are tracked to provide aquantitative determination of its concentration. “Amplification” hererefers to a degree of sensitivity and specificity of a quantitativeassay technique. Accordingly, amplification provides a measurement ofconcentrations of constituents that is evaluated under conditionswherein the efficiency of amplification and therefore the degree ofsensitivity and reproducibility for measuring all constituents issubstantially similar.

A “baseline profile data set” is a set of values associated withconstituents of a Gene Expression Panel resulting from evaluation of abiological sample (or population of samples) under a desired biologicalcondition that is used for mathematically normative purposes. Thedesired biological condition may be, for example, the condition of asubject (or population of subjects) before exposure to an agent or inthe presence of an untreated disease or in the absence of a disease.Alternatively, or in addition, the desired biological condition may behealth of a subject or a population of subjects. Alternatively, or inaddition, the desired biological condition may be that associated with apopulation subjects selected on the basis of at least one of age group,gender, ethnicity, geographic location, diet, medical disorder, clinicalindicator, medication, physical activity, body mass, and environmentalexposure.

A “biological condition” of a subject is the condition of the subject ina pertinent realm that is under observation, and such realm may includeany aspect of the subject capable of being monitored for change incondition, such as health, disease including cancer; trauma; aging;infection; tissue degeneration; developmental steps; physical fitness;obesity, and mood. As can be seen, a condition in this context may bechronic or acute or simply transient. Moreover, a targeted biologicalcondition may be manifest throughout the organism or population of cellsor may be restricted to a specific organ (such as skin, heart, eye orblood), but in either case, the condition may be monitored directly by asample of the affected population of cells or indirectly by a samplederived elsewhere from the subject. The term “biological condition”includes a “physiological condition”.

“Body fluid” of a subject includes blood, urine, spinal fluid, lymph,mucosal secretions, prostatic fluid, semen, haemolymph or any other bodyfluid known in the art for a subject.

“Calibrated profile data set” is a function of a member of a firstprofile data set and a corresponding member of a baseline profile dataset for a given constituent in a panel.

A “clinical indicator” is any physiological datum used alone or inconjunction with other data in evaluating the physiological condition ofa collection of cells or of an organism. This term includes pre-clinicalindicators.

A “composition” includes a chemical compound, a nutriceutical, apharmaceutical, a homeopathic formulation, an allopathic formulation, anaturopathic formulation, a combination of compounds, a toxin, a food, afood supplement, a mineral, and a complex mixture of substances, in anyphysical state or in a combination of physical states.

To “derive” a profile data set from a sample includes determining a setof values associated with constituents of a Gene Expression Panel either(i) by direct measurement of such constituents in a biological sample or(ii) by measurement of such constituents in a second biological samplethat has been exposed to the original sample or to matter derived fromthe original sample.

“Distinct RNA or protein constituent” in a panel of constituents is adistinct expressed product of a gene, whether RNA or protein. An“expression” product of a gene includes the gene product whether RNA orprotein resulting from translation of the messenger RNA.

A “Gene Expression Panel” is an experimentally verified set ofconstituents, each constituent being a distinct expressed product of agene, whether RNA or protein, wherein constituents of the set areselected so that their measurement provides a measurement of a targetedbiological condition.

A “Gene Expression Profile” is a set of values associated withconstituents of a Gene Expression Panel resulting from evaluation of abiological sample (or population of samples).

A “Gene Expression Profile Inflammatory Index” is the value of an indexfunction that provides a mapping from an instance of a Gene ExpressionProfile into a single-valued measure of inflammatory condition.

The “health” of a subject includes mental, emotional, physical,spiritual, allopathic, naturopathic and homeopathic condition of thesubject.

“Index” is an arithmetically or mathematically derived numericalcharacteristic developed for aid in simplifying or disclosing orinforming the analysis of more complex quantitative information. Adisease or population index may be determined by the application of aspecific algorithm to a plurality of subjects or samples with a commonbiological condition.

“Inflammation” is used herein in the general medical sense of the wordand may be an acute or chronic; simple or supporative; localized ordisseminated; cellular and tissue response, initiated or sustained byany number of chemical, physical or biological agents or combination ofagents.

“Inflammatory state” is used to indicate the relative biologicalcondition of a subject resulting from inflammation, or characterizingthe degree of inflammation

A “large number” of data sets based on a common panel of genes is anumber of data sets sufficiently large to permit a statisticallysignificant conclusion to be drawn with respect to an instance of a dataset based on the same panel.

A “normative” condition of a subject to whom a composition is to beadministered means the condition of a subject before administration,even if the subject happens to be suffering from a disease.

A “panel” of genes is a set of genes including at least twoconstituents.

A “sample” from a subject may include a single cell or multiple cells orfragments of cells or an aliquot of body fluid, taken from the subject,by means including venipuncture, excretion, ejaculation, massage,biopsy, needle aspirate, lavage sample, scraping, surgical incision orintervention or other means known in the art.

A “Signature Profile” is an experimentally verified subset of a GeneExpression Profile selected to discriminate a biological condition,agent or physiological mechanism of action.

A “Signature Panel” is a subset of a Gene Expression Panel, theconstituents of which are selected to permit discrimination of abiological condition, agent or physiological mechanism of action.

A “subject” is a cell, tissue, or organism, human or non-human, whetherin vivo, ex vivo or in vitro, under observation. When we refer toevaluating the biological condition of a subject based on a sample fromthe subject, we include using blood or other tissue sample from a humansubject to evaluate the human subject's condition; but we also include,for example, using a blood sample itself as the subject to evaluate, forexample, the effect of therapy or an agent upon the sample.

A “stimulus” includes (i) a monitored physical interaction with asubject, for example ultraviolet A or B, or light therapy for seasonalaffective disorder, or treatment of psoriasis with psoralen or treatmentof melanoma with embedded radioactive seeds, other radiation exposure,and (ii) any monitored physical, mental, emotional, or spiritualactivity or inactivity of a subject.

“Therapy” includes all interventions whether biological, chemical,physical, metaphysical, or combination of the foregoing, intended tosustain or alter the monitored biological condition of a subject.

The PCT patent application publication number WO 01/25473, publishedApr. 12, 2001, entitled “Systems and Methods for Characterizing aBiological Condition or Agent Using Calibrated Gene ExpressionProfiles,” filed for an invention by inventors herein, and which isherein incorporated by reference, discloses the use of Gene ExpressionPanels for the evaluation of (i) biological condition (including withrespect to health and disease) and (ii) the effect of one or more agentson biological condition (including with respect to health, toxicity,therapeutic treatment and drug interaction).

In particular, Gene Expression Panels may be used for measurement oftherapeutic efficacy of natural or synthetic compositions or stimulithat may be formulated individually or in combinations or mixtures for arange of targeted physiological conditions; prediction of toxicologicaleffects and dose effectiveness of a composition or mixture ofcompositions for an individual or in a population; determination of howtwo or more different agents administered in a single treatment mightinteract so as to detect any of synergistic, additive, negative, neutralor toxic activity; performing pre-clinical and clinical trials byproviding new criteria for pre-selecting subjects according toinformative profile data sets for revealing disease status; andconducting preliminary dosage studies for these patients prior toconducting phase 1 or 2 trials. These Gene Expression Panels may beemployed with respect to samples derived from subjects in order toevaluate their biological condition.

A Gene Expression Panel is selected in a manner so that quantitativemeasurement of RNA or protein constituents in the Panel constitutes ameasurement of a biological condition of a subject. In one kind ofarrangement, a calibrated profile data set is employed. Each member ofthe calibrated profile data set is a function of (i) a measure of adistinct constituent of a Gene Expression Panel and (ii) a baselinequantity.

We have found that valuable and unexpected results may be achieved whenthe quantitative measurement of constituents is performed underrepeatable conditions (within a degree of repeatability of measurementof better than twenty percent, and preferably five percent or better,and more preferably three percent or better). For the purposes of thisdescription and the following claims, we regard a degree ofrepeatability of measurement of better than twenty percent as providingmeasurement conditions that are “substantially repeatable”. Inparticular, it is desirable that, each time a measurement is obtainedcorresponding to the level of expression of a constituent in aparticular sample, substantially the same measurement should result forthe substantially the same level of expression. In this manner,expression levels for a constituent in a Gene Expression Panel may bemeaningfully compared from sample to sample. Even if the expressionlevel measurements for a particular constituent are inaccurate (forexample, say, 30% too low), the criterion of repeatability means thatall measurements for this constituent, if skewed, will nevertheless beskewed systematically, and therefore measurements of expression level ofthe constituent may be compared meaningfully. In this fashion valuableinformation may be obtained and compared concerning expression of theconstituent under varied circumstances.

In addition to the criterion of repeatability, it is desirable that asecond criterion also be satisfied, namely that quantitative measurementof constituents is performed under conditions wherein efficiencies ofamplification for all constituents are substantially similar (within oneto two percent and typically one percent or less). When both of thesecriteria are satisfied, then measurement of the expression level of oneconstituent may be meaningfully compared with measurement of theexpression level of another constituent in a given sample and fromsample to sample.

Present embodiments relate to the use of an index or algorithm resultingfrom quantitative measurement of constituents, and optionally inaddition, derived from either expert analysis or computational biology(a) in the analysis of complex data sets; (b) to control or normalizethe influence of uninformative or otherwise minor variances in geneexpression values between samples or subjects; (c) to simplify thecharacterization of a complex data set for comparison to other complexdata sets, databases or indices or algorithms derived from complex datasets; (d) to monitor a biological condition of a subject; (e) formeasurement of therapeutic efficacy of natural or synthetic compositionsor stimuli that may be formulated individually or in combinations ormixtures for a range of targeted physiological conditions; (f) forpredictions of toxicological effects and dose effectiveness of acomposition or mixture of compositions for an individual or in apopulation; (g) for determination of how two or more different agentsadministered in a single treatment might interact so as to detect any ofsynergistic, additive, negative, neutral of toxic activity (h) forperforming pre-clinical and clinical trials by providing new criteriafor pre-selecting subjects according to informative profile data setsfor revealing disease status and conducting preliminary dosage studiesfor these patients prior to conducting phase 1 or 2 trials.

Gene expression profiling and the use of index characterization for aparticular condition or agent or both may be used to reduce the cost ofphase 3 clinical trials and may be used beyond phase 3 trials; labelingfor approved drugs; selection of suitable medication in a class ofmedications for a particular patient that is directed to their uniquephysiology; diagnosing or determining a prognosis of a medical conditionor an infection which may precede onset of symptoms or alternativelydiagnosing adverse side effects associated with administration of atherapeutic agent; managing the health care of a patient; and qualitycontrol for different batches of an agent or a mixture of agents.

The Subject

The methods disclosed here may be applied to cells of humans, mammals orother organisms without the need for undue experimentation by one ofordinary skill in the art because all cells transcribe RNA and it isknown in the art how to extract RNA from all types of cells.

Selecting Constituents of a Gene Expression Panel

The general approach to selecting constituents of a Gene ExpressionPanel has been described in PCT application publication number WO01/25473. We have designed and experimentally verified a wide range ofGene Expression Panels, each panel providing a quantitative measure, ofbiological condition, that is derived from a sample of blood or othertissue. For each panel, experiments have verified that a Gene ExpressionProfile using the panel's constituents is informative of a biologicalcondition. (We show elsewhere that in being informative of biologicalcondition, the Gene Expression Profile can be used to used, among otherthings, to measure the effectiveness of therapy, as well as to provide atarget for therapeutic intervention.) Examples of Gene ExpressionPanels, along with a brief description of each panel constituent, areprovided in tables attached hereto as follows:

Table 1. Inflammation Gene Expression Panel

Table 2. Diabetes Gene Expression Panel

Table 3. Prostate Gene Expression Panel

Table 4. Skin Response Gene Expression Panel

Table 5. Liver Metabolism and Disease Gene Expression Panel

Table 6. Endothelial Gene Expression Panel

Table 7. Cell Health and Apoptosis Gene Expression Panel

Table 8. Cytokine Gene Expression Panel

Table 9. TNF/IL1 Inhibition Gene Expression Panel

Table 10. Chemokine Gene Expression Panel

Table 11. Breast Cancer Gene Expression Panel

Table 12. Infectious Disease Gene Expression Panel

Other panels may be constructed and experimentally verified by one ofordinary skill in the art in accordance with the principles articulatedin the present application.

Design of Assays

We commonly run a sample through a panel in quadruplicate; that is, asample is divided into aliquots and for each aliquot we measureconcentrations of each constituent in a Gene Expression Panel. Over atotal of 900 constituent assays, with each assay conducted inquadruplicate, we found an average coefficient of variation, (standarddeviation/average)*100, of less than 2 percent, typically less than 1percent, among results for each assay. This figure is a measure of whatwe call “intra-assay variability”. We have also conducted assays ondifferent occasions using the same sample material. With 72 assays,resulting from concentration measurements of constituents in a panel of24 members, and such concentration measurements determined on threedifferent occasions over time, we found an average coefficient ofvariation of less than 5 percent, typically less than 2 percent. Weregard this as a measure of what we call “inter-assay variability”.

We have found it valuable in using the quadruplicate test results toidentify and eliminate data points that are statistical “outliers”; suchdata points are those that differ by a percentage greater, for example,than 3% of the average of all four values and that do not result fromany systematic skew that is greater, for example, than 1%. Moreover, ifmore than one data point in a set of four is excluded by this procedure,then all data for the relevant constituent is discarded.

Measurement of Gene Expression for a Constituent in the Panel

For measuring the amount of a particular RNA in a sample, we have usedmethods known to one of ordinary skill in the art to extract andquantify transcribed RNA from a sample with respect to a constituent ofa Gene Expression Panel. (See detailed protocols below. Also see PCTapplication publication number WO 98/24935 herein incorporated byreference for RNA analysis protocols). Briefly, RNA is extracted from asample such as a tissue, body fluid, or culture medium in which apopulation of a subject might be growing. For example, cells may belysed and RNA eluted in a suitable solution in which to conduct a DNAsereaction. First strand synthesis may be performed using a reversetranscriptase. Gene amplification, more specifically quantitative PCRassays, can then conducted and the gene of interest size calibratedagainst a marker such as 18S rRNA (Hirayama et al., Blood 92, 1998:46-52). Samples are measured in multiple duplicates, for example, 4replicates. Relative quantitation of the mRNA is determined by thedifference in threshold cycles between the internal control and the geneof interest In an embodiment of the invention, quantitative PCR isperformed using amplification, reporting agents and instruments such asthose supplied commercially by Applied Biosystems (Foster City, Calif.).Given a defined efficiency of amplification of target transcripts, thepoint (e.g., cycle number) that signal from amplified target template isdetectable may be directly related to the amount of specific messagetranscript in the measured sample. Similarly, other quantifiable signalssuch as fluorescence, enzyme activity, disintegrations per minute,absorbance, etc., when correlated to a known concentration of targettemplates (e.g., a reference standard curve) or normalized to a standardwith limited variability can be used to quantify the number of targettemplates in an unknown sample.

Although not limited to amplification methods, quantitative geneexpression techniques may utilize amplification of the targettranscript. Alternatively or in combination with amplification of thetarget transcript, amplification of the reporter signal may also beused. Amplification of the target template may be accomplished byisothermic gene amplification strategies, or by gene amplification bythermal cycling such as PCR.

It is desirable to obtain a definable and reproducible correlationbetween the amplified target or reporter and the concentration ofstarting templates. We have discovered that this objective can beachieved by careful attention to, for example, consistentprimer-template ratios and a strict adherence to a narrow permissiblelevel of experimental amplification efficiencies (for example 99.0 to100% relative efficiency, typically 99.8 to 100% relative efficiency).For example, in determining gene expression levels with regard to asingle Gene Expression Profile, it is necessary that all constituents ofthe panels maintain a similar and limited range of primer templateratios (for example, within a 10-fold range) and amplificationefficiencies (within, for example, less than 1%) to permit accurate andprecise relative measurements for each constituent. We regardamplification efficiencies as being “substantially similar”, for thepurposes of this description and the following claims, if they differ byno more than approximately 10%. Preferably they should differ by lessthan approximately 2% and more preferably by less than approximately 1%.These constraints should be observed over the entire range ofconcentration levels to be measured associated with the relevantbiological condition. While it is thus necessary for various embodimentsherein to satisfy criteria that measurements are achieved undermeasurement conditions that are substantially repeatable and whereinspecificity and efficiencies of amplification for all constituents aresubstantially similar, nevertheless, it is within the scope of thepresent invention as claimed herein to achieve such measurementconditions by adjusting assay results that do not satisfy these criteriadirectly, in such a manner as to compensate for errors, so that thecriteria are satisfied after suitable adjustment of assay results.

In practice, we run tests to assure that these conditions are satisfied.For example, we typically design and manufacture a number ofprimer-probe sets, and determine experimentally which set gives the bestperformance. Even though primer-probe design and manufacture can beenhanced using computer techniques known in the art, and notwithstandingcommon practice, we still find that experimental validation is useful.Moreover, in the course of experimental validation, we associate withthe selected primer-probe combination a set of features:

The reverse primer should be complementary to the coding DNA strand. Inone embodiment, the primer should be located across an intron-exonjunction, with not more than three bases of the three-prime end of thereverse primer complementary to the proximal exon. (If more than threebases are complementary, then it would tend to competitively amplifygenomic DNA.)

In an embodiment of the invention, the primer probe should amplify cDNAof less than 110 bases in length and should not amplify genomic DNA ortranscripts or cDNA from related but biologically irrelevant loci.

A suitable target of the selected primer probe is first strand cDNA,which may be prepared, in one embodiment, is described as follows:

(a) Use of whole blood for ex vivo assessment of a biological conditionaffected by an agent.

Human blood is obtained by venipuncture and prepared for assay byseparating samples for baseline, no stimulus, and stimulus withsufficient volume for at least three time points. Typical stimuliinclude lipopolysaccharide (LPS), phytohemagglutinin (PHA) andheat-killed staphylococci (HKS) or carrageean and may be usedindividually (typically) or in combination. The aliquots of heparinized,whole blood are mixed without stimulus and held at 37° C. in anatmosphere of 5% CO2 for 30 minutes. Stimulus is added at varyingconcentrations, mixed and held loosely capped at 37° C. for 30 min.Additional test compounds may be added at this point and held forvarying times depending on the expected pharmacokinetics of the testcompound. At defined times, cells are collected by centrifugation, theplasma removed and RNA extracted by various standard means.

Nucleic acids, RNA and or DNA are purified from cells, tissues or fluidsof the test population or indicator cell lines. RNA is preferentiallyobtained from the nucleic acid mix using a variety of standardprocedures (or RNA Isolation Strategies, pp. 55-104, in RNAMethodologies, A laboratory guide for isolation and characterization,2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in thepresent using a filter-based RNA isolation system from Ambion(RNAqueous™, Phenol-free Total RNA Isolation Kit, Catalog #1912, version9908; Austin, Tex.).

In accordance with one procedure, the whole blood assay for GeneExpression Profiles determination was carried out as follows: Humanwhole blood was drawn into 10 mL Vacutainer tubes with Sodium Heparin.Blood samples were mixed by gently inverting tubes 4-5 times. The bloodwas used within 10-15 minutes of draw. In the experiments, blood wasdiluted 2-fold, i.e. per sample per time point, 0.6 mL whole blood+0.6mL stimulus. The assay medium was prepared and the stimulus added asappropriate.

A quantity (0.6 mL) of whole blood was then added into each 12×75 mmpolypropylene tube. 0.6 mL of 2×LPS (from E. coli serotye 0127:B8, Sigma#L3880 or serotype 055, Sigma #L4005, 10 ng/ml, subject to change indifferent lots) into LPS tubes was added. Next, 0.6 mL assay medium wasadded to the “control” tubes with duplicate tubes for each condition.The caps were closed tightly. The tubes were inverted 2-3 times to mixsamples. Caps were loosened to first stop and the tubes incubated @ 37°C., 5% CO2 for 6 hours. At 6 hours, samples were gently mixed toresuspend blood cells, and 1 mL was removed from each tube (using amicropipettor with barrier tip), and transferred to a 2 mL “dolphin”microfuge tube (Costar #3213).

The samples were then centrifuged for 5 min at 500×g, ambienttemperature (IEC centrifuge or equivalent, in microfuge tube adapters inswinging bucket), and as much serum from each tube was removed aspossible and discarded. Cell pellets were placed on ice; and RNAextracted as soon as possible using an Ambion RNAqueous kit.

(b) Amplification Strategies.

Specific RNAs are amplified using message specific primers or randomprimers. The specific primers are synthesized from data obtained frompublic databases (e.g., Unigene, National Center for BiotechnologyInformation, National Library of Medicine, Bethesda, Md.), includinginformation from genomic and cDNA libraries obtained from humans andother animals. Primers are chosen to preferentially amplify fromspecific RNAs obtained from the test or indicator samples, see, forexample, RT PCR, Chapter 15 in RNA Methodologies, A laboratory guide forisolation and characterization, 2nd edition, 1998, Robert E. Farrell,Jr., Ed., Academic Press; or Chapter 22 pp. 143-151, RNA isolation andcharacterization protocols, Methods in molecular biology, Volume 86,1998, R. Rapley and D. L. Manning Eds., Human Press, or 14 inStatistical refinement of primer design parameters, Chapter 5, pp.55-72, PCR applications: protocols for functional genomics, M. A. Innis,D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press).Amplifications are carried out in either isothermic conditions or usinga thermal cycler (for example, a ABI 9600 or 9700 or 7700 obtained fromApplied Biosystems, Foster City, Calif.; see Nucleic acid detectionmethods, pp. 1-24, in Molecular methods for virus detection, D. L.Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplifiednucleic acids are detected using fluorescent-tagged detection primers(see, for example, Taqman™ PCR Reagent Kit, Protocol, part number 402823revision A, 1996, Applied Biosystems, Foster City Calif.) that areidentified and synthesized from publicly known databases as describedfor the amplification primers. In the present case, amplified DNA isdetected and quantified using the ABI Prism 7700 Sequence DetectionSystem obtained from Applied Biosystems (Foster City, Calif.). Amountsof specific RNAs contained in the test sample or obtained from theindicator cell lines can be related to the relative quantity offluorescence observed (see for example, Advances in quantitative PCRtechnology: 5′ nuclease assays, Y. S. Lie and C. J. Petropolus, CurrentOpinion in Biotechnology, 1998, 9:43-48, or Rapid thermal cycling andPCR kinetics, pp. 211-229, chapter 14 in PCR applications: protocols forfunctional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds.,1999, Academic Press).

As a particular implementation of the approach described here, wedescribe in detail a procedure for synthesis of first strand cDNA foruse in PCR. This procedure can be used for both whole blood RNA and RNAextracted from cultured cells (i.e. THP-1 cells).

Materials

1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N808-0234). Kit Components: 10× TaqMan RT Buffer, 25 mM Magnesiumchloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor,MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase/DNase free water(DEPC Treated Water from Ambion (P/N 9915G), or equivalent)

Methods

1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on iceimmediately. All other reagents can be thawed at room temperature andthen placed on ice.

2. Remove RNA samples from −80° C. freezer and thaw at room temperatureand then place immediately on ice.

3. Prepare the following cocktail of Reverse Transcriptase Reagents foreach 100 mL RT reaction (for multiple samples, prepare extra cocktail toallow for pipetting error):

1 reaction (mL) 11X, e.g. 10 samples (mL) 10X RT Buffer 10.0 110.0 25 mMMgCl2 22.0 242.0 dNTPs 20.0 220.0 Random Hexamers 5.0 55.0 RNAseInhibitor 2.0 22.0 Reverse Transcriptase 2.5 27.5 Water 18.5 203.5Total: 80.0 880.0 (80 mL per sample)

4. Bring each RNA sample to a total volume of 20 mL in a 1.5 mLmicrocentrifuge tube (for example, for THP-1 RNA, remove 10 mL RNA anddilute to 20 mL with RNase/DNase free water, for whole blood RNA use 20mL total RNA) and add 80 mL RT reaction mix from step 5, 2, 3. Mix bypipetting up and down.

5. Incubate sample at room temperature for 10 minutes.

6. Incubate sample at 37° C. for 1 hour.

7. Incubate sample at 90° C. for 10 minutes.

8. Quick spin samples in microcentrifuge.

9. Place sample on ice if doing PCR immediately, otherwise store sampleat −20° C. for future use.

10. PCR QC should be run on all RT samples using 18S and b-actin (seeSOP 200-020).

The use of the primer probe with the first strand cDNA as describedabove to permit measurement of constituents of a Gene Expression Panelis as follows:

Set up of a 24-gene Human Gene Expression Panel for Inflammation.

Materials

1. 20× Primer/Probe Mix for each gene of interest.

2. 20× Primer/Probe Mix for 18S endogenous control.

3. 2× Taqman Universal PCR Master Mix.

4. cDNA transcribed from RNA extracted from cells.

5. Applied Biosystems 96-Well Optical Reaction Plates.

6. Applied Biosystems Optical Caps, or optical-clear film.

7. Applied Biosystem Prism 7700 Sequence Detector.

Methods

1. Make stocks of each Primer/Probe mix containing the Primer/Probe forthe gene of interest, Primer/Probe for 18S endogenous control, and 2×PCRMaster Mix as follows. Make sufficient excess to allow for pipettingerror e.g. approximately 10% excess. The following example illustrates atypical set up for one gene with quadruplicate samples testing twoconditions (2 plates).

1X 9X (1 well) (2 plates worth) 2X Master Mix 12.50 112.50 20X 18SPrimer/Probe Mix 1.25 11.25 20X Gene of interest Primer/Probe Mix 1.2511.25 Total 15.00 135.00

2. Make stocks of cDNA targets by diluting 95 μl of cDNA into 2000 μl ofwater. The amount of cDNA is adjusted to give Ct values between 10 and18, typically between 12 and 13.

3. Pipette 15 μl of Primer/Probe mix into the appropriate wells of anApplied Biosystems 96-Well Optical Reaction Plate.

4. Pipette 10 μl of cDNA stock solution into each well of the AppliedBiosystems 96-Well Optical Reaction Plate.

5. Seal the plate with Applied Biosystems Optical Caps, or optical-clearfilm.

6. Analyze the plate on the AB Prism 7700 Sequence Detector.

Methods herein may also be applied using proteins where sensitivequantitative techniques, such as an Enzyme Linked ImmunoSorbent Assay(ELISA) or mass spectroscopy, are available and well-known in the artfor measuring the amount of a protein constituent. (see WO 98/24935herein incorporated by reference).

Baseline Profile Data Sets

The analyses of samples from single individuals and from large groups ofindividuals provide a library of profile data sets relating to aparticular panel or series of panels. These profile data sets may bestored as records in a library for use as baseline profile data sets. Asthe term “baseline” suggests, the stored baseline profile data setsserve as comparators for providing a calibrated profile data set that isinformative about a biological condition or agent. Baseline profile datasets may be stored in libraries and classified in a number ofcross-referential ways. One form of classification may rely on thecharacteristics of the panels from which the data sets are derived.Another form of classification may be by particular biologicalcondition. The concept of biological condition encompasses any state inwhich a cell or population of cells may be found at any one time. Thisstate may reflect geography of samples, sex of subjects or any otherdiscriminator. Some of the discriminators may overlap. The libraries mayalso be accessed for records associated with a single subject orparticular clinical trial. The classification of baseline profile datasets may further be annotated with medical information about aparticular subject, a medical condition, a particular agent etc.

The choice of a baseline profile data set for creating a calibratedprofile data set is related to the biological condition to be evaluated,monitored, or predicted, as well as, the intended use of the calibratedpanel, e.g., as to monitor drug development, quality control or otheruses. It may be desirable to access baseline profile data sets from thesame subject for whom a first profile data set is obtained or fromdifferent subject at varying times, exposures to stimuli, drugs orcomplex compounds; or may be derived from like or dissimilarpopulations.

The profile data set may arise from the same subject for which the firstdata set is obtained, where the sample is taken at a separate or similartime, a different or similar site or in a different or similarphysiological condition. For example, FIG. 5 provides a protocol inwhich the sample is taken before stimulation or after stimulation. Theprofile data set obtained from the unstimulated sample may serve as abaseline profile data set for the sample taken after stimulation. Thebaseline data set may also be derived from a library containing profiledata sets of a population of subjects having some definingcharacteristic or biological condition. The baseline profile data setmay also correspond to some ex vivo or in vitro properties associatedwith an in vitro cell culture. The resultant calibrated profile datasets may then be stored as a record in a database or library (FIG. 6)along with or separate from the baseline profile data base andoptionally the first profile data set although the first profile dataset would normally become incorporated into a baseline profile data setunder suitable classification criteria. The remarkable consistency ofGene Expression Profiles associated with a given biological conditionmakes it valuable to store profile data, which can be used, among otherthings for normative reference purposes. The normative reference canserve to indicate the degree to which a subject conforms to a givenbiological condition (healthy or diseased) and, alternatively or inaddition, to provide a target for clinical intervention.

Selected baseline profile data sets may be also be used as a standard bywhich to judge manufacturing lots in terms of efficacy, toxicity, etc.Where the effect of a therapeutic agent is being measured, the baselinedata set may correspond to Gene Expression Profiles taken beforeadministration of the agent. Where quality control for a newlymanufactured product is being determined, the baseline data set maycorrespond with a gold standard for that product. However, any suitablenormalization techniques may be employed. For example, an averagebaseline profile data set is obtained from authentic material of anaturally grown herbal nutriceutical and compared over time and overdifferent lots in order to demonstrate consistency, or lack ofconsistency, in lots of compounds prepared for release.

Calibrated Data

Given the repeatability we have achieved in measurement of geneexpression, described above in connection with “Gene Expression Panels”and “gene amplification”, we conclude that where differences occur inmeasurement under such conditions, the differences are attributable todifferences in biological condition. Thus we have found that calibratedprofile data sets are highly reproducible in samples taken from the sameindividual under the same conditions. We have similarly found thatcalibrated profile data sets are reproducible in samples that arerepeatedly tested. We have also found repeated instances whereincalibrated profile data sets obtained when samples from a subject areexposed ex vivo to a compound are comparable to calibrated profile datafrom a sample that has been exposed to a sample in vivo. We have alsofound, importantly, that an indicator cell line treated with an agentcan in many cases provide calibrated profile data sets comparable tothose obtained from in vivo or ex vivo populations of cells. Moreover,we have found that administering a sample from a subject onto indicatorcells can provide informative calibrated profile data sets with respectto the biological condition of the subject including the health, diseasestates, therapeutic interventions, aging or exposure to environmentalstimuli or toxins of the subject.

Calculation of Calibrated Profile Data Sets and Computational Aids

The calibrated profile data set may be expressed in a spreadsheet orrepresented graphically for example, in a bar chart or tabular form butmay also be expressed in a three dimensional representation. Thefunction relating the baseline and profile data may be a ratio expressedas a logarithm. The constituent may be itemized on the x-axis and thelogarithmic scale may be on the y-axis. Members of a calibrated data setmay be expressed as a positive value representing a relative enhancementof gene expression or as a negative value representing a relativereduction in gene expression with respect to the baseline.

Each member of the calibrated profile data set should be reproduciblewithin a range with respect to similar samples taken from the subjectunder similar conditions. For example, the calibrated profile data setsmay be reproducible within one order of magnitude with respect tosimilar samples taken from the subject under similar conditions. Moreparticularly, the members may be reproducible within 50%, moreparticularly reproducible within 20%, and typically within 10%. Inaccordance with embodiments of the invention, a pattern of increasing,decreasing and no change in relative gene expression from each of aplurality of gene loci examined in the Gene Expression Panel may be usedto prepare a calibrated profile set that is informative with regards toa biological condition, biological efficacy of an agent treatmentconditions or for comparison to populations. Patterns of this nature maybe used to identify likely candidates for a drug trial, used alone or incombination with other clinical indicators to be diagnostic orprognostic with respect to a biological condition or may be used toguide the development of a pharmaceutical or nutriceutical throughmanufacture, testing and marketing.

The numerical data obtained from quantitative gene expression andnumerical data from calibrated gene expression relative to a baselineprofile data set may be stored in databases or digital storage mediumsand may retrieved for purposes including managing patient health care orfor conducting clinical trials or for characterizing a drug. The datamay be transferred in physical or wireless networks via the World WideWeb, email, or internet access site for example or by hard copy so as tobe collected and pooled from distant geographic sites (FIG. 8).

In an embodiment of the present invention, a descriptive record isstored in a single database or multiple databases where the stored dataincludes the raw gene expression data (first profile data set) prior totransformation by use of a baseline profile data set, as well as arecord of the baseline profile data set used to generate the calibratedprofile data set including for example, annotations regarding whetherthe baseline profile data set is derived from a particular SignaturePanel and any other annotation that facilitates interpretation and useof the data.

Because the data is in a universal format, data handling may readily bedone with a computer. The data is organized so as to provide an outputoptionally corresponding to a graphical representation of a calibrateddata set.

For example, a distinct sample derived from a subject being at least oneof RNA or protein may be denoted as P_(I). The first profile data setderived from sample P_(I) is denoted M_(j), where M_(j) is aquantitative measure of a distinct RNA or protein constituent of P_(I).The record Ri is a ratio of M and P and may be annotated with additionaldata on the subject relating to, for example, age, diet, ethnicity,gender, geographic location, medical disorder, mental disorder,medication, physical activity, body mass and environmental exposure.Moreover, data handling may further include accessing data from a secondcondition database which may contain additional medical data notpresently held with the calibrated profile data sets. In this context,data access may be via a computer network.

The above described data storage on a computer may provide theinformation in a form that can be accessed by a user. Accordingly, theuser may load the information onto a second access site includingdownloading the information. However, access may be restricted to usershaving a password or other security device so as to protect the medicalrecords contained within. A feature of this embodiment of the inventionis the ability of a user to add new or annotated records to the data setso the records become part of the biological information.

The graphical representation of calibrated profile data sets pertainingto a product such as a drug provides an opportunity for standardizing aproduct by means of the calibrated profile, more particularly asignature profile. The profile may be used as a feature with which todemonstrate relative efficacy, differences in mechanisms of actions,etc. compared to other drugs approved for similar or different uses.

The various embodiments of the invention may be also implemented as acomputer program product for use with a computer system. The product mayinclude program code for deriving a first profile data set and forproducing calibrated profiles. Such implementation may include a seriesof computer instructions fixed either on a tangible medium, such as acomputer readable medium (for example, a diskette, CD-ROM, ROM, or fixeddisk), or transmittable to a computer system via a modem or otherinterface device, such as a communications adapter coupled to a network.The network coupling may be for example, over optical or wiredcommunications lines or via wireless techniques (for example, microwave,infrared or other transmission techniques) or some combination of these.The series of computer instructions preferably embodies all or part ofthe functionality previously described herein with respect to thesystem. Those skilled in the art should appreciate that such computerinstructions can be written in a number of programming languages for usewith many computer architectures or operating systems. Furthermore, suchinstructions may be stored in any memory device, such as semiconductor,magnetic, optical or other memory devices, and may be transmitted usingany communications technology, such as optical, infrared, microwave, orother transmission technologies. It is expected that such a computerprogram product may be distributed as a removable medium withaccompanying printed or electronic documentation (for example, shrinkwrapped software), preloaded with a computer system (for example, onsystem ROM or fixed disk), or distributed from a server or electronicbulletin board over a network (for example, the Internet or World WideWeb). In addition, a computer system is further provided includingderivative modules for deriving a first data set and a calibrationprofile data set.

The calibration profile data sets in graphical or tabular form, theassociated databases, and the calculated index or derived algorithm,together with information extracted from the panels, the databases, thedata sets or the indices or algorithms are commodities that can be soldtogether or separately for a variety of purposes as described in WO01/25473.

Index Construction

In combination, (i) the remarkable consistency of Gene ExpressionProfiles with respect to a biological condition across a population and(ii) the use of procedures that provide substantially reproduciblemeasurement of constituents in a Gene Expression Panel giving rise to aGene Expression Profile, under measurement conditions whereinspecificity and efficiencies of amplification for all constituents ofthe panel are substantially similar, make possible the use of an indexthat characterizes a Gene Expression Profile, and which thereforeprovides a measurement of a biological condition.

An index may be constructed using an index function that maps values ina Gene Expression Profile into a single value that is pertinent to thebiological condition at hand. The values in a Gene Expression Profileare the amounts of each constituent of the Gene Expression Panel thatcorresponds to the Gene Expression Profile. These constituent amountsform a profile data set, and the index function generates a singlevalue—the index—from the members of the profile data set.

The index function may conveniently be constructed as a linear sum ofterms, each term being what we call a “contribution function” of amember of the profile data set. For example, the contribution functionmay be a constant times a power of a member of the profile data set. Sothe index function would have the formI=ΣC _(i) M _(i) ^(P(i)),where I is the index, M_(i) is the value of the member i of the profiledata set, C_(i) is a constant, and P(i) is a power to which M_(i) israised, the sum being formed for all integral values of i up to thenumber of members in the data set. We thus have a linear polynomialexpression.

The values C_(i) and P(i) may be determined in a number of ways, so thatthe index I is informative of the pertinent biological condition. Oneway is to apply statistical techniques, such as latent class modeling,to the profile data sets to correlate clinical data or experimentallyderived data, or other data pertinent to the biological condition. Inthis connection, for example, may be employed the software fromStatistical Innovations, Belmont, Mass., called Latent Gold®. See theweb pages at www.statisticalinnovations.com/lg/, which are herebyincorporated herein by reference.

Alternatively, other simpler modeling techniques may be employed in amanner known in the art. The index function for inflammation may beconstructed, for example, in a manner that a greater degree ofinflammation (as determined by the a profile data set for theInflammation Gene Expression Profile) correlates with a large value ofthe index function. In a simple embodiment, therefore, each P(i) may be+1 or −1, depending on whether the constituent increases or decreaseswith increasing inflammation. As discussed in further detail below, wehave constructed a meaningful inflammation index that is proportional tothe expression¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10},where the braces around a constituent designate measurement of suchconstituent and the constituents are a subset of the Inflammation GeneExpression Panel of Table 1.

Just as a baseline profile data set, discussed above, can be used toprovide an appropriate normative reference, and can even be used tocreate a Calibrated profile data set, as discussed above, based on thenormative reference, an index that characterizes a Gene ExpressionProfile can also be provided with a normative value of the indexfunction used to create the index. This normative value can bedetermined with respect to a relevant population, so that the index maybe interpreted in relation to the normative value. The relevantpopulation may have in common a property that is at least one of agegroup, gender, ethnicity, geographic location, diet, medical disorder,clinical indicator, medication, physical activity, body mass, andenvironmental exposure.

As an example, the index can be constructed, in relation to a normativeGene Expression Profile for a population of healthy subjects, in such away that a reading of approximately 1 characterizes normative GeneExpression Profiles of healthy subjects. Let us further assume that thebiological condition that is the subject of the index is inflammation; areading of 1 in this example thus corresponds to a Gene ExpressionProfile that matches the norm for healthy subjects. A substantiallyhigher reading then may identify a subject experiencing an inflammatorycondition. The use of 1 as identifying a normative value, however, isonly one possible choice; another logical choice is to use 0 asidentifying the normative value. With this choice, deviations in theindex from zero can be indicated in standard deviation units (so thatvalues lying between −1 and +1 encompass 90% of a normally distributedreference population. Since we have found that Gene Expression Profilevalues (and accordingly constructed indices based on them) tend to benormally distributed, the 0-centered index constructed in this manner ishighly informative. It therefore facilitates use of the index indiagnosis of disease and setting objectives for treatment. The choice of0 for the normative value, and the use of standard deviation units, forexample, are illustrated in FIG. 17B, discussed below.

Examples Example 1

Acute Inflammatory Index to Assist in Analysis of Large, Complex DataSets. In one embodiment of the invention the index value or algorithmcan be used to reduce a complex data set to a single index value that isinformative with respect to the inflammatory state of a subject. This isillustrated in FIGS. 1A and 1B.

FIG. 1A is entitled Source Precision Inflammation Profile Tracking of ASubject Results in a Large, Complex Data Set. The figure shows theresults of assaying 24 genes from the Inflammation Gene Expression Panel(shown in Table 1) on eight separate days during the course of opticneuritis in a single male subject.

FIG. 1B shows use of an Acute Inflammation Index. The data displayed inFIG. 1A above is shown in this figure after calculation using an indexfunction proportional to the following mathematical expression:(¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}).

Example 2

Use of acute inflammation index or algorithm to monitor a biologicalcondition of a sample or a subject. The inflammatory state of a subjectreveals information about the past progress of the biological condition,future progress, response to treatment, etc. The Acute InflammationIndex may be used to reveal such information about the biologicalcondition of a subject. This is illustrated in FIG. 2.

The results of the assay for inflammatory gene expression for each day(shown for 24 genes in each row of FIG. 1A) is displayed as anindividual histogram after calculation. The index reveals clear trendsin inflammatory status that may correlated with therapeutic intervention(FIG. 2).

FIG. 2 is a graphical illustration of the acute inflammation indexcalculated at 9 different, significant clinical milestones from bloodobtained from a single patient treated medically with for opticneuritis. Changes in the index values for the Acute Inflammation Indexcorrelate strongly with the expected effects of therapeuticintervention. Four clinical milestones have been identified on top ofthe Acute Inflammation Index in this figure including (1) prior totreatment with steroids, (2) treatment with IV solumedrol at 1 gram perday, (3) post-treatment with oral prednisone at 60 mg per day tapered to10 mg per day and (4) post treatment. The data set is the same as forFIG. 1. The index is proportional to¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}. As expected, the acuteinflammation index falls rapidly with treatment with IV steroid, goes upduring less efficacious treatment with oral prednisone and returns tothe pre-treatment level after the steroids have been discontinued andmetabolized completely.

Example 3

Use of the acute inflammatory index to set dose, includingconcentrations and timing, for compounds in development or for compoundsto be tested in human and non-human subjects as shown in FIG. 3. Theacute inflammation index may be used as a common reference value fortherapeutic compounds or interventions without common mechanisms ofaction. The compound that induces a gene response to a compound asindicated by the index, but fails to ameliorate a known biologicalconditions may be compared to a different compounds with varyingeffectiveness in treating the biological condition.

FIG. 3 shows the effects of single dose treatment with 800 mg ofibuprofen in a single donor as characterized by the Acute InflammationIndex. 800 mg of over-the-counter ibuprofen were taken by a singlesubject at Time=0 and Time=48 hr. Gene expression values for theindicated five inflammation-related gene loci were determined asdescribed below at times=2, 4, 6, 48, 50, 56 and 96 hours. As expectedthe acute inflammation index falls immediately after taking thenon-steroidal anti-inflammatory ibuprofen and returns to baseline after48 hours. A second dose at T=48 follows the same kinetics at the firstdose and returns to baseline at the end of the experiment at T=96.

Example 4

Use of the acute inflammation index to characterize efficacy, safety,and mode of physiological action for an agent, which may be indevelopment and/or may be complex in nature. This is illustrated in FIG.4.

FIG. 4 shows that the calculated acute inflammation index displayedgraphically for five different conditions including (A) untreated wholeblood; (B) whole blood treated in vitro with DMSO, an non-active carriercompound; (C) otherwise unstimulated whole blood treated in vitro withdexamethasone (0.08 ug/ml); (D) whole blood stimulated in vitro withlipopolysaccharide, a known pro-inflammatory compound, (LPS, 1 ng/ml)and (E) whole blood treated in vitro with LPS (1 ng/ml) anddexamethasone (0.08 ug/ml). Dexamethasone is used as a prescriptioncompound that is commonly used medically as an anti-inflammatory steroidcompound. The acute inflammation index is calculated from theexperimentally determined gene expression levels of inflammation-relatedgenes expressed in human whole blood obtained from a single patient.Results of mRNA expression are expressed as Ct's in this example, butmay be expressed as, e.g., relative fluorescence units, copy number orany other quantifiable, precise and calibrated form, for the genes IL1A,IL1B, TNF, IFNG and IL10. From the gene expression values, the acuteinflammation values were determined algebraically according inproportion to the expression ¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}.

Example 5

Development and use of population normative values for Gene ExpressionProfiles. FIGS. 6 and 7 show the arithmetic mean values for geneexpression profiles (using the 48 loci of the Inflammation GeneExpression Panel of Table 1) obtained from whole blood of two distinctpatient populations. These populations are both normal or undiagnosed.The first population, which is identified as Bonfils (the plot pointsfor which are represented by diamonds), is composed of 17 subjectsaccepted as blood donors at the Bonfils Blood Center in Denver, Colo.The second population is 9 donors, for which Gene Expression Profileswere obtained from assays conducted four times over a four-week period.Subjects in this second population (plot points for which arerepresented by squares) were recruited from employees of SourcePrecision Medicine, Inc., the assignee herein. Gene expression averagesfor each population were calculated for each of 48 gene loci of the GeneExpression Inflammation Panel. The results for loci 1-24 (sometimesreferred to below as the Inflammation 48A loci) are shown in FIG. 6 andfor loci 25-48 (sometimes referred to below as the Inflammation 48Bloci) are shown in FIG. 7.

The consistency between gene expression levels of the two distinctpopulations is dramatic. Both populations show gene expressions for eachof the 48 loci that are not significantly different from each other.This observation suggests that there is a “normal” expression patternfor human inflammatory genes, that a Gene Expression Profile, using theInflammation Gene Expression Panel of Table 1 (or a subset thereof)characterizes that expression pattern, and that a population-normalexpression pattern can be used, for example, to guide medicalintervention for any biological condition that results in a change fromthe normal expression pattern.

In a similar vein, FIG. 8 shows arithmetic mean values for geneexpression profiles (again using the 48 loci of the Inflammation GeneExpression Panel of Table 1) also obtained from whole blood of twodistinct patient populations. One population, expression values forwhich are represented by triangular data points, is 24 normal,undiagnosed subjects (who therefore have no known inflammatory disease).The other population, the expression values for which are represented bydiamond-shaped data points, is four patients with rheumatoid arthritisand who have failed therapy (who therefore have unstable rheumatoidarthritis).

As remarkable as the consistency of data from the two distinct normalpopulations shown in FIGS. 6 and 7 is the systematic divergence of datafrom the normal and diseased populations shown in FIG. 8. In 45 of theshown 48 inflammatory gene loci, subjects with unstable rheumatoidarthritis showed, on average, increased inflammatory gene expression(lower cycle threshold values; Ct), than subjects without disease. Thedata thus further demonstrate that is possible to identify groups withspecific biological conditions using gene expression if the precisionand calibration of the underlying assay are carefully designed andcontrolled according to the teachings herein.

FIG. 9, in a manner analogous to FIG. 8, shows the shows arithmetic meanvalues for gene expression profiles using 24 loci of the InflammationGene Expression Panel of Table 1) also obtained from whole blood of twodistinct patient populations. One population, expression values forwhich are represented by diamond-shaped data points, is 17 normal,undiagnosed subjects (who therefore have no known inflammatory disease)who are blood donors. The other population, the expression values forwhich are represented by square-shaped data points, is 16 subjects, alsonormal and undiagnosed, who have been monitored over six months, and theaverages of these expression values are represented by the square-shapeddata points. Thus the cross-sectional gene expression-value averages ofa first healthy population match closely the longitudinal geneexpression-value averages of a second healthy population, withapproximately 7% or less variation in measured expression value on agene-to-gene basis.

FIG. 10 shows the shows gene expression values (using 14 loci of theInflammation Gene Expression Panel of Table 1) obtained from whole bloodof 44 normal undiagnosed blood donors (data for 10 subjects of which isshown). Again, the gene expression values for each member of thepopulation are closely matched to those for the population, representedvisually by the consistent peak heights for each of the gene loci. Othersubjects of the population and other gene loci than those depicted heredisplay results that are consistent with those shown here.

In consequence of these principles, and in various embodiments of thepresent invention, population normative values for a Gene ExpressionProfile can be used in comparative assessment of individual subjects asto biological condition, including both for purposes of health and/ordisease. In one embodiment the normative values for a Gene ExpressionProfile may be used as a baseline in computing a “calibrated profiledata set” (as defined at the beginning of this section) for a subjectthat reveals the deviation of such subject's gene expression frompopulation normative values. Population normative values for a GeneExpression Profile can also be used as baseline values in constructingindex functions in accordance with embodiments of the present invention.As a result, for example, an index function can be constructed to revealnot only the extent of an individual's inflammation expression generallybut also in relation to normative values.

Example 6

Consistency of expression values, of constituents in Gene ExpressionPanels, over time as reliable indicators of biological condition. FIG.11 shows the expression levels for each of four genes (of theInflammation Gene Expression Panel of Table 1), of a single subject,assayed monthly over a period of eight months. It can be seen that theexpression levels are remarkably consistent over time.

FIGS. 12 and 13 similarly show in each case the expression levels foreach of 48 genes (of the Inflammation Gene Expression Panel of Table 1),of distinct single subjects (selected in each case on the basis offeeling well and not taking drugs), assayed, in the case of FIG. 12weekly over a period of four weeks, and in the case of FIG. 13 monthlyover a period of six months. In each case, again the expression levelsare remarkably consistent over time, and also similar acrossindividuals.

FIG. 14 also shows the effect over time, on inflammatory gene expressionin a single human subject, of the administration of an anti-inflammatorysteroid, as assayed using the Inflammation Gene Expression Panel ofTable 1. In this case, 24 of 48 loci are displayed. The subject had abaseline blood sample drawn in a PAX RNA isolation tube and then took asingle 60 mg dose of prednisone, an anti-inflammatory, prescriptionsteroid. Additional blood samples were drawn at 2 hr and 24 hr post thesingle oral dose. Results for gene expression are displayed for allthree time points, wherein values for the baseline sample are shown asunity on the x-axis. As expected, oral treatment with prednisoneresulted in the decreased expression of most of inflammation-relatedgene loci, as shown by the 2-hour post-administration bar graphs.However, the 24-hour post-administration bar graphs show that, for mostof the gene loci having reduced gene expression at 2 hours, there wereelevated gene expression levels at 24 hr.

Although the baseline in FIG. 14 is based on the gene expression valuesbefore drug intervention associated with the single individual tested,we know from the previous example, that healthy individuals tend towardpopulation normative values in a Gene Expression Profile using theInflammation Gene Expression Panel of Table 1 (or a subset of it). Weconclude from FIG. 14 that in an attempt to return the inflammatory geneexpression levels to those demonstrated in FIGS. 6 and 7 (normal or setlevels), interference with the normal expression induced a compensatorygene expression response that over-compensated for the drug-inducedresponse, perhaps because the prednisone had been significantlymetabolized to inactive forms or eliminated from the subject.

FIG. 15, in a manner analogous to FIG. 14, shows the effect over time,via whole blood samples obtained from a human subject, administered asingle dose of prednisone, on expression of 5 genes (of the InflammationGene Expression Panel of Table 1). The samples were taken at the time ofadministration (t=0) of the prednisone, then at two and 24 hours aftersuch administration. Each whole blood sample was challenged by theaddition of 0.1 ng/ml of lipopolysaccharide (a Gram-negative endotoxin)and a gene expression profile of the sample, post-challenge, wasdetermined. It can seen that the two-hour sample shows dramaticallyreduced gene expression of the 5 loci of the Inflammation GeneExpression Panel, in relation to the expression levels at the time ofadministration (t=0). At 24 hours post administration, the inhibitoryeffect of the prednisone is no longer apparent, and at 3 of the 5 loci,gene expression is in fact higher than at t=0, illustratingquantitatively at the molecular level the well-known rebound effect.

FIG. 16 also shows the effect over time, on inflammatory gene expressionin a single human subject suffering from rheumatoid arthritis, of theadministration of a TNF-inhibiting compound, but here the expression isshown in comparison to the cognate locus average previously determined(in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed,healthy) population. As part of a larger international study involvingpatients with rheumatoid arthritis, the subject was followed over atwelve-week period. The subject was enrolled in the study because of afailure to respond to conservative drug therapy for rheumatoid arthritisand a plan to change therapy and begin immediate treatment with aTNF-inhibiting compound. Blood was drawn from the subject prior toinitiation of new therapy (visit 1). After initiation of new therapy,blood was drawn at 4 weeks post change in therapy (visit 2), 8 weeks(visit 3), and 12 weeks (visit 4) following the start of new therapy.Blood was collected in PAX RNA isolation tubes, held at room temperaturefor two hours and then frozen at −30° C.

Frozen samples were shipped to the central laboratory at SourcePrecision Medicine, the assignee herein, in Boulder, Colo. fordetermination of expression levels of genes in the 48-gene InflammationGene Expression Panel of Table 1. The blood samples were thawed and RNAextracted according to the manufacturer's recommended procedure. RNA wasconverted to cDNA and the level of expression of the 48 inflammatorygenes was determined. Expression results are shown for 11 of the 48 lociin FIG. 16. When the expression results for the 11 loci are comparedfrom visit one to a population average of normal blood donors from theUnited States, the subject shows considerable difference. Similarly,gene expression levels at each of the subsequent physician visits foreach locus are compared to the same normal average value. Data fromvisits 2, 3 and 4 document the effect of the change in therapy. In eachvisit following the change in the therapy, the level of inflammatorygene expression for 10 of the 11 loci is closer to the cognate locusaverage previously determined for the normal (i.e., undiagnosed,healthy) population.

FIG. 17A further illustrates the consistency of inflammatory geneexpression, illustrated here with respect to 7 loci of (of theInflammation Gene Expression Panel of Table 1), in a population of 44normal, undiagnosed blood donors. For each individual locus is shown therange of values lying within ±2 standard deviations of the meanexpression value, which corresponds to 95% of a normally distributedpopulation. Notwithstanding the great width of the confidence interval(95%), the measured gene expression value (ΔCT)—remarkably—still lieswithin 10% of the mean, regardless of the expression level involved. Asdescribed in further detail below, for a given biological condition anindex can be constructed to provide a measurement of the condition. Thisis possible as a result of the conjunction of two circumstances: (i)there is a remarkable consistency of Gene Expression Profiles withrespect to a biological condition across a population and (ii) there canbe employed procedures that provide substantially reproduciblemeasurement of constituents in a Gene Expression Panel giving rise to aGene Expression Profile, under measurement conditions whereinspecificity and efficiencies of amplification for all constituents ofthe panel are substantially similar and which therefore provides ameasurement of a biological condition. Accordingly, a function of theexpression values of representative constituent loci of FIG. 17A is hereused to generate an inflammation index value, which is normalized sothat a reading of 1 corresponds to constituent expression values ofhealthy subjects, as shown in the right-hand portion of FIG. 17A.

In FIG. 17B, an inflammation index value was determined for each memberof a population of 42 normal undiagnosed blood donors, and the resultingdistribution of index values, shown in the figure, can be seen toapproximate closely a normal distribution, notwithstanding therelatively small population size. The values of the index are shownrelative to a 0-based median, with deviations from the median calibratedin standard deviation units. Thus 90% of the population lies within +1and −1 of a 0 value. We have constructed various indices, which exhibitsimilar behavior.

FIG. 17C illustrates the use of the same index as FIG. 17B, where theinflammation median for a normal population has been set to zero andboth normal and diseased subjects are plotted in standard deviationunits relative to that median. An inflammation index value wasdetermined for each member of a normal, undiagnosed population of 70individuals (black bars). The resulting distribution of index values,shown in FIG. 17C, can be seen to approximate closely a normaldistribution. Similarly, index values were calculated for individualsfrom two diseased population groups, (1) rheumatoid arthritis patientstreated with methotrexate (MTX) who are about to change therapy to moreefficacious drugs (e.g., TNF inhibitors) (hatched bars), and (2)rheumatoid arthritis patients treated with disease modifyinganti-rheumatoid drugs (DMARDS) other than MTX, who are about to changetherapy to more efficacious drugs (e.g., MTX). Both populations presentindex values that are skewed upward (demonstrating increasedinflammation) in comparison to the normal distribution. This figure thusillustrates the utility of an index to derived from Gene ExpressionProfile data to evaluate disease status and to provide an objective andquantifiable treatment objective. When these two populations weretreated appropriately, index values from both populations returned to amore normal distribution (data not shown here).

FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene ExpressionProfiles, for the same 7 loci as in FIG. 17A, two different 6-subjectpopulations of rheumatoid arthritis patients. One population (called“stable” in the figure) is of patients who have responded well totreatment and the other population (called “unstable” in the figure) isof patients who have not responded well to treatment and whose therapyis scheduled for change. It can be seen that the expression values forthe stable population, lie within the range of the 95% confidenceinterval, whereas the expression values for the unstable population for5 of the 7 loci are outside and above this range. The right-hand portionof the figure shows an average inflammation index of 9.3 for theunstable population and an average inflammation index of 1.8 for thestable population, compared to 1 for a normal undiagnosed population.The index thus provides a measure of the extent of the underlyinginflammatory condition, in this case, rheumatoid arthritis. Hence theindex, besides providing a measure of biological condition, can be usedto measure the effectiveness of therapy as well as to provide a targetfor therapeutic intervention.

FIG. 19 thus illustrates use of the inflammation index for assessment ofa single subject suffering from rheumatoid arthritis, who has notresponded well to traditional therapy with methotrexate. Theinflammation index for this subject is shown on the far right at startof a new therapy (a TNF inhibitor), and then, moving leftward,successively, 2 weeks, 6 weeks, and 12 weeks thereafter. The index canbe seen moving towards normal, consistent with physician observation ofthe patient as responding to the new treatment.

FIG. 20 similarly illustrates use of the inflammation index forassessment of three subjects suffering from rheumatoid arthritis, whohave not responded well to traditional therapy with methotrexate, at thebeginning of new treatment (also with a TNF inhibitor), and 2 weeks and6 weeks thereafter. The index in each case can again be seen movinggenerally towards normal, consistent with physician observation of thepatients as responding to the new treatment.

Each of FIGS. 21-23 shows the inflammation index for an internationalgroup of subjects, suffering from rheumatoid arthritis, each of whom hasbeen characterized as stable (that is, not anticipated to be subjectedto a change in therapy) by the subject's treating physician. FIG. 21shows the index for each of 10 patients in the group being treated withmethotrexate, which known to alleviate symptoms without addressing theunderlying disease. FIG. 22 shows the index for each of 10 patients inthe group being treated with Enbrel (an TNF inhibitor), and FIG. 23shows the index for each 10 patients being treated with Remicade(another TNF inhibitor). It can be seen that the inflammation index foreach of the patients in FIG. 21 is elevated compared to normal, whereasin FIG. 22, the patients being treated with Enbrel as a class have aninflammation index that comes much closer to normal (80% in the normalrange). In FIG. 23, it can be seen that, while all but one of thepatients being treated with Remicade have an inflammation index at orbelow normal, two of the patients have an abnormally low inflammationindex, suggesting an immunosuppressive response to this drug. (Indeed,studies have shown that Remicade has been associated with seriousinfections in some subjects, and here the immunosuppressive effect isquantified.) Also in FIG. 23, one subject has an inflammation index thatis significantly above the normal range. This subject in fact was alsoon a regimen of an anti-inflammation steroid (prednisone) that was beingtapered; within approximately one week after the inflammation index wassampled, the subject experienced a significant flare of clinicalsymptoms.

Remarkably, these examples show a measurement, derived from the assay ofblood taken from a subject, pertinent to the subject's arthriticcondition. Given that the measurement pertains to the extent ofinflammation, it can be expected that other inflammation-basedconditions, including, for example, cardiovascular disease, may bemonitored in a similar fashion.

FIG. 24 illustrates use of the inflammation index for assessment of asingle subject suffering from inflammatory bowel disease, for whomtreatment with Remicade was initiated in three doses. The graphs showthe inflammation index just prior to first treatment, and then 24 hoursafter the first treatment; the index has returned to the normal range.The index was elevated just prior to the second dose, but in the normalrange prior to the third dose. Again, the index, besides providing ameasure of biological condition, is here used to measure theeffectiveness of therapy (Remicade), as well as to provide a target fortherapeutic intervention in terms of both dose and schedule.

FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of theInflammation Gene Expression Panel of Table 1) for whole blood treatedwith Ibuprofen in vitro in relation to other non-steroidalanti-inflammatory drugs (NSAIDs). The profile for Ibuprofen is in front.It can be seen that all of the NSAIDs, including Ibuprofen share asubstantially similar profile, in that the patterns of gene expressionacross the loci are similar. Notwithstanding these similarities, eachindividual drug has its own distinctive signature.

FIG. 26 illustrates how the effects of two competing anti-inflammatorycompounds can be compared objectively, quantitatively, precisely, andreproducibly. In this example, expression of each of a panel of twogenes (of the Inflammation Gene Expression Panel of Table 1) is measuredfor varying doses (0.08-250 μg/ml) of each drug in vitro in whole blood.The market leader drug shows a complex relationship between dose andinflammatory gene response. Paradoxically, as the dose is increased,gene expression for both loci initially drops and then increases in thecase the case of the market leader. For the other compound, a moreconsistent response results, so that as the dose is increased, the geneexpression for both loci decreases more consistently.

FIGS. 27 through 41 illustrate the use of gene expression panels inearly identification and monitoring of infectious disease. These figuresplot the response, in expression products of the genes indicated, inwhole blood, to the administration of various infectious agents orproducts associated with infectious agents. In each figure, the geneexpression levels are “calibrated”, as that term is defined herein, inrelation to baseline expression levels determined with respect to thewhole blood prior to administration of the relevant infectious agent. Inthis respect the figures are similar in nature to various figures of ourbelow-referenced patent application WO 01/25473 (for example, FIG. 15therein). The concentration change is shown ratiometrically, and thebaseline level of 1 for a particular gene locus corresponds to anexpression level for such locus that is the same, monitored at therelevant time after addition of the infectious agent or other stimulus,as the expression level before addition of the stimulus. Ratiometricchanges in concentration are plotted on a logarithmic scale. Bars belowthe unity line represent decreases in concentration and bars above theunity line represent increases in concentration, the magnitude of eachbar indicating the magnitude of the ratio of the change. We have shownin WO 01/25473 and other experiments that, under appropriate conditions,Gene Expression Profiles derived in vitro by exposing whole blood to astimulus can be representative of Gene Expression Profiles derived invivo with exposure to a corresponding stimulus.

FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes,developed to discriminate various bacterial conditions in a hostbiological system. Two different stimuli are employed: lipotechoic acid(LTA), a gram positive cell wall constituent, and lipopolysaccharide(LPS), a gram negative cell wall constituent. The final concentrationimmediately after administration of the stimulus was 100 ng/mL, and theratiometric changes in expression, in relation to pre-administrationlevels, were monitored for each stimulus 2 and 6 hours afteradministration. It can be seen that differential expression can beobserved as early as two hours after administration, for example, in theIFNA2 locus, as well as others, permitting discrimination in responsebetween gram positive and gram negative bacteria.

FIG. 28 shows differential expression for a single locus, IFNG, to LTAderived from three distinct sources: S. pyogenes, B. subtilis, and S.aureus. Each stimulus was administered to achieve a concentration of 100ng/mL, and the response was monitored at 1, 2, 4, 6, and 24 hours afteradministration. The results suggest that Gene Expression Profiles can beused to distinguish among different infectious agents, here differentspecies of gram positive bacteria.

FIGS. 29 and 30 show the response of the Inflammation 48A and 48B locirespectively (discussed above in connection with FIGS. 6 and 7respectively) in whole blood to administration of a stimulus of S.aureus and of a stimulus of E. coli (in the indicated concentrations,just after administration, of 10⁷ and 10⁶ CFU/mL respectively),monitored 2 hours after administration in relation to thepre-administration baseline. The figures show that many of the locirespond to the presence of the bacterial infection within two hoursafter infection.

FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and aresimilar to them, with the exception that the monitoring here occurs 6hours after administration. More of the loci are responsive to thepresence of infection. Various loci, such as IL2, show expression levelsthat discriminate between the two infectious agents.

FIG. 33 shows the response of the Inflammation 48A loci to theadministration of a stimulus of E. coli (again in the concentration justafter administration of 10⁶ CFU/mL) and to the administration of astimulus of an E. coli filtrate containing E. coli bacteria by productsbut lacking E. coli bacteria. The responses were monitored at 2, 6, and24 hours after administration. It can be seen, for example, that theresponses over time of loci IL1B, IL18 and CSF3 to E.coli and to E. colifiltrate are different.

FIG. 34 is similar to FIG. 33, but here the compared responses are tostimuli from E. coli filtrate alone and from E. coli filtrate to whichhas been added polymyxin B, an antibiotic known to bind tolipopolysaccharide (LPS). An examination of the response of IL1B, forexample, shows that presence of polymyxin B did not affect the responseof the locus to E. coli filtrate, thereby indicating that LPS does notappear to be a factor in the response of IL1B to E. coli filtrate.

FIG. 35 illustrates the responses of the Inflammation 48A loci over timeof whole blood to a stimulus of S. aureus (with a concentration justafter administration of 10⁷ CFU/mL) monitored at 2, 6, and 24 hoursafter administration. It can be seen that response over time can involveboth direction and magnitude of change in expression. (See for example,IL5 and IL18.)

FIGS. 36 and 37 show the responses, of the Inflammation 48A and 48B locirespectively, monitored at 6 hours to stimuli from E. coli (atconcentrations of 10⁶ and 10² CFU/mL immediately after administration)and from S. aureus (at concentrations of 10⁷ and 10² CFU/mL immediatelyafter administration). It can be seen, among other things, that invarious loci, such as B7 (FIG. 36), TACI, PLA2G7, and C1QA (FIG. 37), E.coli produces a much more pronounced response than S. aureus. The datasuggest strongly that Gene Expression Profiles can be used to identifywith high sensitivity the presence of gram negative bacteria and todiscriminate against gram positive bacteria.

FIGS. 38 and 39 show the responses, of the Inflammation 48B and 48A locirespectively, monitored 2, 6, and 24 hours after administration, tostimuli of high concentrations of S. aureus and E. coli respectively (atrespective concentrations of 10⁷ and 10⁶ CFU/mL immediately afteradministration). The responses over time at many loci involve changes inmagnitude and direction. FIG. 40 is similar to FIG. 39, but shows theresponses of the Inflammation 48B loci.

FIG. 41 similarly shows the responses of the Inflammation 48A locimonitored at 24 hours after administration to stimuli highconcentrations of S. aureus and E. coli respectively (at respectiveconcentrations of 10⁷ and 10⁶ CFU/mL immediately after administration).As in the case of FIGS. 20 and 21, responses at some loci, such as GRO1and GRO2, discriminate between type of infection.

These data support our conclusion that Gene Expression Profiles withsufficient precision and calibration as described herein (1) candetermine subpopulations of individuals with a known biologicalcondition; (2) may be used to monitor the response of patients totherapy; (3) may be used to assess the efficacy and safety of therapy;and (4) may used to guide the medical management of a patient byadjusting therapy to bring one or more relevant Gene Expression Profilescloser to a target set of values, which may be normative values or otherdesired or achievable values. We have shown that Gene ExpressionProfiles may provide meaningful information even when derived from exvivo treatment of blood or other tissue. We have also shown that GeneExpression Profiles derived from peripheral whole blood are informativeof a wide range of conditions neither directly nor typically associatedwith blood.

Furthermore, in embodiments of the present invention, Gene ExpressionProfiles can also be used for characterization and early identification(including pre-symptomatic states) of infectious disease, such assepsis. This characterization includes discriminating between infectedand uninfected individuals, bacterial and viral infections, specificsubtypes of pathogenic agents, stages of the natural history ofinfection (e.g., early or late), and prognosis. Use of the algorithmicand statistical approaches discussed above to achieve suchidentification and to discriminate in such fashion is within the scopeof various embodiments herein.

TABLE 1 Inflammation Gene Expression Panel Symbol Name ClassificationDescription IL1A Interleukin 1, cytokines- Proinflammatory;constitutively and alpha chemokines-growth inducibly expressed invariety of cells. factors Generally cytosolic and released only duringsevere inflammatory disease IL1B Interleukin 1, cytokines-Proinflammatory; constitutively and beta chemokines-growth induciblyexpressed by many cell types, factors secreted TNFA Tumor necrosiscytokines- Proinflammatory, TH1, mediates host factor, alphachemokines-growth response to bacterial stimulus, regulates factors cellgrowth & differentiation IL6 Interleukin 6 cytokines- Pro- andantiinflammatory activity, TH2 (interferon, chemokines-growth cytokine,regulates hemotopoietic beta 2) factors system and activation of innateresponse IL8 Interleukin 8 cytokines- Proinflammatory, major secondarychemokines-growth inflammatory mediator, cell adhesion, factors signaltransduction, cell-cell signaling, angiogenesis, synthesized by a widevariety of cell types IFNG Interferon cytokines- Pro- andantiinflammatory activity, TH1 gamma chemokines-growth cytokine,nonspecific inflammatory factors mediator, produced by activated T-cellsIL2 Interleukin 2 cytokines- T-cell growth factor, expressed bychemokines-growth activated T-cells, regulates lymphocyte factorsactivation and differentiation; inhibits apoptosis, TH1 cytokine IL12BInterleukin 12 cytokines- Proinflammatory; mediator of innate p40chemokines-growth immunity, TH1 cytokine, requires co- factorsstimulation with IL-18 to induce IFN-g IL15 Interleukin 15 cytokines-Proinflammatory; mediates T-cell chemokines-growth activation, inhibitsapoptosis, synergizes factors with IL-2 to induce IFN-g and TNF-a IL18Interleukin 18 cytokines- Proinflammatory, TH1, innate andchemokines-growth aquired immunity, promotes apoptosis, factors requiresco-stimulation with IL-1 or IL- 2 to induce TH1 cytokines in T- andNK-cells IL4 Interleukin 4 cytokines- Antiinflammatory; TH2; suppresseschemokines-growth proinflammatory cytokines, increases factorsexpression of IL-1RN, regulates lymphocyte activation IL5 Interleukin 5cytokines- Eosinophil stimulatory factor; chemokines-growth stimulateslate B cell differentiation to factors secretion of Ig IL10 Interleukin10 cytokines- Antiinflammatory; TH2; suppresses chemokines-growthproduction of proinflammatory factors cytokines IL13 Interleukin 13cytokines- Inhibits inflammatory cytokine chemokines-growth productionfactors IL1RN Interleukin 1 cytokines- IL1 receptor antagonist; receptorchemokines-growth Antiinflammatory; inhibits binding of antagonistfactors IL-1 to IL-1 receptor by binding to receptor without stimulatingIL-1-like activity IL18BP IL-18 Binding cytokines- Implicated ininhibition of early TH1 Protein chemokines-growth cytokine responsesfactors TGFB1 Transforming cytokines- Pro- and antiinflammatoryactivity, anti- growth factor, chemokines-growth apoptotic; cell-cellsignaling, can either beta 1 factors inhibit or stimulate cell growthIFNA2 Interferon, cytokines- interferon produced by macrophages alpha 2chemokines-growth with antiviral effects factors GRO1 GRO1 oncogenecytokines- AKA SCYB1; chemotactic for (melanoma chemokines-growthneutrophils growth factors stimulating activity, alpha) GRO2 GRO2oncogene cytokines- AKA MIP2, SCYB2; Macrophage chemokines-growthinflammatory protein produced by factors moncytes and neutrophils TNFSF5Tumor necrosis cytokines- ligand for CD40; expressed on the factor(ligand) chemokines-growth surface of T cells. It regulates B cellsuperfamily, factors function by engaging CD40 on the B member 5 cellsurface TNFSF6 Tumor necrosis cytokines- AKA FasL; Ligand for FASantigen; factor (ligand) chemokines-growth transduces apoptotic signalsinto cells superfamily, factors member 6 CSF3 Colony cytokines- AKAGCSF; cytokine that stimulates stimulating chemokines-growth granulocytedevelopment factor 3 factors (granulocyte) B7 B7 protein cell signalingand Regulatory protein that may be activation associated with lupus CSF2Granulocyte- cytokines- AKA GM-CSF; Hematopoietic growth monocytechemokines-growth factor; stimulates growth and colony factorsdifferentiation of hematopoietic stimulating precursor cells fromvarious lineages, factor including granulocytes, macrophages,eosinophils, and erythrocytes TNFSF13B Tumor necrosis cytokines- B cellactivating factor, TNF family factor (ligand) chemokines-growthsuperfamily, factors member 13b TACI Transmembrane cytokines- T cellactivating factor and calcium activator and chemokines-growthcyclophilin modulator CAML factors interactor VEGF vascular cytokines-Producted by monocytes endothelial chemokines-growth growth factorfactors ICAM1 Intercellular Cell Adhesion/ Endothelial cell surfacemolecule; adhesion Matrix Protein regulates cell adhesion andtrafficking, molecule 1 upregulated during cytokine stimulation PTGS2Prostaglandin- Enzyme/Redox AKA COX2; Proinflammatory, memberendoperoxide of arachidonic acid to prostanoid synthase 2 conversionpathway; induced by proinflammatory cytokines NOS2A Nitric oxideEnzyme/Redox AKA iNOS; produces NO which is synthase 2Abacteriocidal/tumoricidal PLA2G7 Phospholipase Enzyme/Redox Plateletactivating factor A2, group VII (platelet activating factoracetylhydrolase, plasma) HMOX1 Heme oxygenase Enzyme/Redox Endotoxininducible (decycling) 1 F3 F3 Enzyme/Redox AKA thromboplastin,Coagulation Factor 3; cell surface glycoprotein responsible forcoagulation catalysis CD3Z CD3 antigen, Cell Marker T-cell surfaceglycoprotein zeta polypeptide PTPRC protein tyrosine Cell Marker AKACD45; mediates T-cell activation phosphatase, receptor type, C CD14 CD14antigen Cell Marker LPS receptor used as marker for monocytes CD4 CD4antigen Cell Marker Helper T-cell marker (p55) CD8A CD8 antigen, CellMarker Suppressor T cell marker alpha polypeptide CD19 CD19 antigen CellMarker AKA Leu 12; B cell growth factor HSPA1A Heat shock Cell Signalingand heat shock protein 70 kDa protein 70 activation MMP3 MatrixProteinase/Proteinase AKA stromelysin; degrades fibronectin,metalloproteinase 3 Inhibitor laminin and gelatin MMP9 MatrixProteinase/Proteinase AKA gelatinase B; degrades metalloproteinase 9Inhibitor extracellular matrix molecules, secreted by IL-8-stimulatedneutrophils PLAU Plasminogen Proteinase/Proteinase AKA uPA; cleavesplasminogen to activator, Inhibitor plasmin (a protease responsible forurokinase nonspecific extracellular matrix degradation) SERPINE1 Serine(or Proteinase/Proteinase Plasminogen activator inhibitor-1/PAI-1cysteine) Inhibitor protease inhibitor, clade B (ovalbumin), member 1TIMP1 tissue inhibitor Proteinase/Proteinase Irreversibly binds andinhibits of Inhibitor metalloproteinases, such as collagenasemetalloproteinase 1 C1QA Complement Proteinase/Proteinase Serumcomplement system; forms C1 component 1, q Inhibitor complex with theproenzymes c1r and subcomponent, c1s alpha polypeptide HLA-DRB1 MajorHistocompatibility Binds antigen for presentation to CD4+histocompatibility cells complex, class II, DR beta 1

TABLE 2 Diabetes Gene Expression Panel Symbol Name ClassificationDescription G6PC glucose-6- Glucose-6- Catalyzes the final step in thephosphatase, phosphatase/Glycogen gluconeogenic and catalytic metabolismglycogenolytic pathways. Stimulated by glucocorticoids and stronglyinhibited by insulin. Overexpression (in conjunction with PCK1overexpression) leads to increased hepatic glucose production. GCGglucagon pancreatic/peptide hormone Pancreatic hormone which counteractsthe glucose- lowering action of insulin by stimulating glycogenolysisand gluconeogenesis. Underexpression of glucagon is preferred.Glucagon-like peptide (GLP-1) proposed for type 2 diabetes treatmentinhibits glucag GCGR glucagon receptor glucagon receptor Expression ofGCGR is strongly upregulated by glucose. Deficiency or imbalance couldplay a role in NIDDM. Has been looked as a potential for gene therapy.GFPT1 glutamine-fructose- Glutamine amidotransferase The rate limitingenzyme for 6-phosphate glucose entry into the transaminase 1 hexosaminebiosynthetic pathway (HBP). Overexpression of GFA in muscle and adiposetissue increases products of the HBP which are thought to cause insulinresistance (possibly through defects to glucose GYS1 glycogen synthase 1Transferase/Glycogen A key enzyme in the (muscle) metabolism regulationof glycogen synthesis in the skeletal muscles of humans. Typicallystimulated by insulin, but in NIDDM individuals GS is shown to becompletely resistant to insulin stimulation (decreased activity andactivation in muscle) HK2 hexokinase 2 hexokinase Phosphorylates glucoseinto glucose-6-phosphate. NIDDM patients have lower HK2 activity whichmay contribute to insulin resistance. Similar action to GCK. INS insulinInsulin receptor ligand Decreases blood glucose concentration andaccelerates glycogen synthesis in the liver. Not as critical in NIDDM asin IDDM. IRS1 insulin receptor signal Positive regultion of insulinsubstrate 1 transduction/transmembrane action. This protein is receptorprotein activated when insulin binds to insulin receptor - binds 85-kDasubunit of PI 3-K. decreased in skeletal muscle of obese humans. PCK1phosphoenolpyruvate rate-limiting gluconeogenic Rate limiting enzyme forcarboxykinase 1 enzyme gluconeogenesis - plays a key role in theregulation of hepatic glucose output by insulin and glucagon.Overexpression in the liver results in increased hepatic glucoseproduction and hepatic insulin resistance to glycogen synthe PIK3R1phosphoinositide-3- regulatory enzyme Positive regulation of insulinkinase, regulatory action. Docks in IRS proteins subunit, polypeptideand Gab1 - activity is required 1 (p85 alpha) for insulin stimulatedtranslocation of glucose transporters to the plasma membrane andactivation of glucose uptake. PPARG peroxisome transcriptionfactor/Ligand- The primary pharmacological proliferator-activateddependent nuclear receptor target for the treatment of receptor, gammainsulin resistance in NIDDM. Involved in glucose and lipid metabolism inskeletal muscle. PRKCB1 protein kinase C, protein kinase C/proteinNegative regulation of insulin beta 1 phosphorylation action. Activatedby hyperglycemia - increases phosphorylation of IRS-1 and reducesinsulin receptor kinase activity. Increased PKC activation may lead tooxidative stress causing overexpression of TGF-beta and fibronectinSLC2A2 solute carrier family glucose transporter Glucose transporters 2(facilitated glucose expressed uniquely in b-cells transporter), member2 and liver. Transport glucose into the b-cell. Typically underexpressedin pancreatic islet cells of individuals with NIDDM. SLC2A4 solutecarrier family glucose transporter Glucose transporter protein 2(facilitated glucose that is final mediator in transporter), member 4insulin-stimulated glucose uptake (rate limiting for glucose uptake).Underexpression not important, but overexpression in muscle and adiposetissue consistently shown to increase glucose transport. TGFB1transforming growth Transforming growth factor Regulated by glucose - infactor, beta 1 beta receptor ligand NIDDM individuals, overexpression(due to oxidative stress - see PKC) promotes renal cell hypertrophyleading to diabetic nephropathy. TNF tumor necrosis factorcytokine/tumor necrosis Negative regulation of insulin factor receptorligand action. Produced in excess by adipose tissue of obeseindividuals - increases IRS-1 phosphorylation and decreases insulinreceptor kinase activity.

TABLE 3 Prostate Gene Expression Panel Symbol Name ClassificationDescription ABCC1 ATP-binding membrane transporter AKA MRP1, ABC29:cassette, sub-family Multispecific organic anion C, member 1 membranetransporter; overexpression confers tissue protection against a widevariety of xenobiotics due to their removal from the cell. ACPP Acidphosphatase, phosphatase AKA PAP: Major prostate phosphatase of theprostate; synthesized under androgen regulation; secreted by theepithelial cells of the prostrate BCL2 B-cell CLL/ apoptosisInhibitor-cell Blocks apoptosis by lymphoma 2 cycle control- interferingwith the oncogenesis activation of caspases BIRC5 Baculoviral IAPapoptosis Inhibitor AKA Survivin; API4: May repeat-containing 5counteract a default induction of apoptosis in G2/M phase of cell cycle;associates with microtubules of the mitotic spindle during apoptosisCDH1 Cadherin 1, type 1, cell-cell adhesion/ AKA ECAD, UVO: E-cadherininteraction Calcium ion-dependent cell adhesion molecule that mediatescell to cell interactions in epithelial cells CDH2 Cadherin 2, type 1,cell-cell adhesion/ AKA NCAD, CDHN: N-cadherin interactionCalcium-dependent glycoprotein that mediates cell-cell interactions; maybe involved in neuronal recognition mechanism CDKN2A Cyclin-dependentcell cycle control- AKA p16, MTS1, INK4: kinase inhibitor 2A tumorsuppressor Tumor suppressor gene involved in a variety of malignancies;arrests normal diploid cells in late G1 CTNNA1 Catenin, alpha 1 celladhesion Binds cadherins and links them with the actin cytoskeletonFOLH1 Folate Hydrolase hydrolase AKA PSMA, GCP2: Expressed in normal andneoplastic prostate cells; membrane bound glycoprotein; hydrolyzesfolate and is an N-acetylated a-linked acidic dipeptidase GSTT1Glutathione-S- metabolism Catalyzes the conjugation of Transferase,theta 1 reduced glutathione to a wide number of exogenous and endogenoushydrophobic electrophiles; has an important role in human carcinogenesisHMGIY High mobility group DNA binding- Potential oncogene with protein,isoforms I transcriptional MYC binding site at and Y regulation-oncogenepromoter region; involved in the transcription regulation of genescontaining, or in close proximity to a+t-rich regions HSPA1A Heat shock70 kD cell signalling and AKA HSP-70, HSP70-1: protein 1A activationMolecular chaperone, stabilizes AU rich mRNA IGF1R Insulin-like growthcytokines-chemokines- Mediates insulin stimulated factor 1 receptorgrowth factors DNA synthesis; mediates IGF1 stimulated cellproliferation and differentiation IL6 Interleukin 6cytokines-chemokines- Pro- and anti-inflammatory growth factorsactivity, TH2 cytokine, regulates hematopoiesis, activation of innateresponse, osteoclast development; elevated in sera of patients withmetastatic cancer IL8 Interleukin 8 cytokines-chemokines- AKA SCYB8,MDNCF: growth factors Proinflammatory chemokine; major secondaryinflammatory mediator resulting in cell adhesion, signal transduction,cell-cell signaling; regulates angiogenesis in prostate cancer KAI1Kangai 1 tumor suppressor AKA SAR2, CD82, ST6: suppressor of metastaticability of prostate cancer cells KLK2 Kallikrein 2, protease-kallikreinAKA hGK-1: Glandular prostatic kallikrein; expression restricted mainlyto the prostate. KLK3 Kallikrein 3 protease-kallikrein AKA PSA:Kallikrein-like protease which functions normally in liquefaction ofseminal fluid. Elevated in prostate cancer. KRT19 Keratin 19 structuralprotein- AKA K19: Type I differentiation epidermal keratin; may formintermediate filaments KRT5 Keratin 5 structural protein- AKA EBS2: 58kD Type II differentiation keratin co-expressed with keratin 14, a 50 kDType I keratin, in stratified epithelium. KRT5 expression is a hallmarkof mitotically active keratinocytes and is the primary structuralcomponent of the 10 nm intermediate filaments of the mitotic epidermalbasal cells. KRT8 Keratin 8 structural protein- AKA K8, CK8: Type IIdifferentiation keratin; coexpressed with Keratin 18; involved inintermediate filament formation LGALS8 Lectin, Galactoside- celladhesion-growth AKA PCTA-1: binds to beta binding, soluble 8 anddifferentiation galactoside; involved in biological processes such ascell adhesion, cell growth regulation, inflammation, immunomodulation,apoptosis and metastasis MYC V-myc avian transcription factor-Transcription factor that myelocytomatosis oncogene promotes cellproliferation viral oncogene and transformation by homolog activatinggrowth- promoting genes; may also repress gene expression NRP1Neuropilin 1 cell adhesion AKA NRP, VEGF165R: A novel VEGF receptor thatmodulates VEGF binding to KDR (VEGF receptor) and subsequent bioactivityand therefore may regulate VEGF-induced angiogenesis; calcium-independent cell adhesion molecule that function during the formation ofcertain neuronal circuits PART1 Prostate androgen- Exhibits increasedregulated transcript 1 expression in LNCaP cells upon exposure toandrogens PCA3 Prostate cancer AKA DD3: prostate antigen 3 specific;highly expressed in prostate tumors PCANAP7 Prostate cancer AKA IPCA7:unknown associated protein 7 function; co-expressed with known prostatecancer genes PDEF Prostate epithelium transcription factor Acts as anandrogen- specific Ets independent transcriptional transcription factoractivator of the PSA promoter; directly interacts with the DNA bindingdomain of androgen receptor and enhances androgen-mediated activation ofthe PSA promoter PLAU Urokinase-type proteinase AKA UPA, URK: cleavesplasminogen plasminogen to plasmin activator POV1 Prostate cancer RNAexpressed selectively overexpressed gene 1 in prostate tumor samplesPSCA Prostate stem cell antigen Prostate-specific cell antigen surfaceantigen expressed strongly by both androgen- dependent and -independenttumors PTGS2 Prostaglandin- cytokines-chemokines- AKA COX-2:endoperoxide growth factors Proinflammatory; member synthase 2 ofarachidonic acid to prostanoid conversion pathway SERPINB5 Serineproteinase proteinase inhibitor- AKA Maspin, PI5: Protease inhibitor,clade B, tumor suppressor Inhibitor; Tumor member 5 suppressor,especially for metastasis. SERPINE1 Serine (or cystein) proteinaseinhibitor AKA PAI1: regulates proteinase inhibitor, fibrinolysis;inhibits PLAU clade E, member 1 STAT3 Signal transduction transcriptionfactor AKA APRF: Transcription and activator of factor for acute phasetranscription 3 response genes; rapidly activated in response to certaincytokines and growth factors; binds to IL6 response elements TERTTelomerase reverse AKA TCS1, EST2: transcriptase Ribonucleoprotein whichin vitro recognizes a single- stranded G-rich telomere primer and addsmultiple telomeric repeats to its 3- prime end by using an RNA templateTGFB1 Transforming cytokines-chemokines- AKA DPD1, CED: Pro- and growthfactor, beta 1 growth factors antiinflammatory activity; anti-apoptotic;cell-cell signaling, can either inhibitor stimulate cell growth TNFTumor necrosis cytokines-chemokines- AKA TNF alpha: factor, member 2growth factors Proinflammatory cytokine that is the primary mediator ofimmune response and regulation, associated with TH1 responses, mediateshost response to bacterial stimuli, regulates cell growth &differentiation TP53 Tumor protein 53 DNA binding protein- AKA P53:Activates cell cycle-tumor expression of genes that suppressor inhibittumor growth and/or invasion; involved in cell cycle regulation(required for growth arrest at G1); inhibits cell growth throughactivation of cell-cycle arrest and apoptosis VEGF Vascular cytokines-AKA VPF: Endothelial chemokines- Induces vascular Growth growthpermeability, Factor factors endothelial cell proliferation,angiogenesis

TABLE 4 Skin Response Gene Expression Panel Symbol Name ClassificationDescription BAX BCL2 apoptosis induction Accelerates associated X germcell development programmed cell protein death by binding to andantagonizing the apoptosis repressor BCL2; may induce caspase activationBCL2 B-cell apoptosis inhibitor- Integral CLL/lymphoma 2 cell cyclecontrol- mitochondrial oncogenesis membrane protein that blocks theapoptotic death of some cells such as lymphocytes; constitutiveexpression of BCL2 thought to be cause of follicular lymphoma BSGBasignin signal transduction- Member of Ig peripheral plasmasuperfamily; tumor membrane protein cell-derived collagenase stimulatoryfactor; stimulates matrix metalloproteinase synthesis in fibroblastsCOL7A1 Type VII collagen- alpha 1 subunit of collagen, alpha 1differentiation- type VII collagen; extracellular matrix may linkcollagen fibrils to the basement membrane CRABP2 Cellular retinoidbinding- Low molecular Retinoic Acid signal transduction- weight proteinhighly Binding Protein transcription expressed in skin; regulationthought to be important in RA- mediated regulation of skin growth &differentiation CTGF Connective insulin-like growth Member of family ofTissue Growth factor- peptides including Factor differentiation-serum-induced wounding response immediate early gene products expressedafter induction by growth factors; overexpressed in fibrotic disordersDUSP1 Dual Specificity oxidative stress Induced in human Phosphataseresponse-tyrosine skin fibroblasts by phosphatase oxidative/heat stress& growth factors; de- phosphorylates MAP kinase erk2; may play a role innegative regulation of cellular proliferation FGF7 Fibroblast growthfactor- aka KGF; Potent growth factor 7 differentiation- mitogen forepithelial wounding response- cells; induced after signal transductionskin injury FN1 Fibronectin cell adhesion- Major cell surfacemotility-signal glycoprotein of many transduction fibroblast cells;thought to have a role in cell adhesion, morphology, wound healing &cell motility FOS v-fos FBJ transcription factor- Proto-oncoproteinmurine inflammatory acting with JUN, osteosarcoma response-cellstimulates virus oncogene growth & transcription of genes homologmaintanence with AP-1 regulatory sites; in some cases FOS expression isassociated with apototic cell death GADD45A Growth Arrest cell cycle-DNATranscriptionally and DNA- repair-apoptosis induced following damage-stressful growth arrest inducible alpha conditions & treatment with DNAdamaging agents; binds to PCNA affecting it's interaction with some celldivision protein kinase GRO1 GRO1 cytokines- AKA SCYB1; oncogenechemokines-growth chemotactic for (melanoma factors neutrophils growthstimulating activity, alpha) HMOX1 Heme metabolism- Essential enzyme inOxygenase 1 endoplasmic heme catabolism; reticulum HMOX1 induced by itssubstrate heme & other substances such as oxidizing agents & UVA ICAM1Intercellular Cell Adhesion/ Endothelial cell adhesion Matrix Proteinsurface molecule; molecule 1 regulates cell adhesion and trafficking,upregulated during cytokine stimulation IL1A Interleukin 1, cytokines-Proinflammatory; alpha chemokines-growth constitutively and factorsinducibly expressed in variety of cells. Generally cytosolic andreleased only during severe inflammatory disease IL1B Interleukin 1,cytokines- Proinflammatory; constitutively beta chemokines-growth andfactors inducibly expressed by many cell types, secreted IL8 Interleukin8 cytokines- Proinflammatory, chemokines-growth major secondary factorsinflammatory mediator, cell adhesion, signal transduction, cell-cellsignaling, angiogenesis, synthesized by a wide variety of cell types IVLInvolucrin structural protein- Component of the peripheral plasmakeratinocyte membrane protein crosslinked envelope; first appears in thecytosol becoming crosslinked to membrane proteins by transglutaminaseJUN v-jun avian transcription factor- Proto-oncoprotein; sarcoma virusDNA binding component of 17 oncogene transcription factor homolog AP-1that interacts directly with target DNA sequences to regulate geneexpression KRT14 Keratin 14 structural protein- Type I keratin;differentiation-cell associates with shape keratin 5; component ofintermediate filaments; several autosomal dominant blistering skindisorders caused by gene defects KRT16 Keratin 16 structural protein-Type I keratin; differentiation-cell component of shape intermediatefilaments; induced in skin conditions favoring enhanced proliferation orabnormal differentiation KRT5 Keratin 5 structural protein- Type IIintermediate differentiation-cell filament chain shape expessed largelyin stratified epithelium; hallmark of mitotically active keratinocytesMAPK8 Mitogen kinase-stress aka JNK1; mitogen Activated response-signalactivated protein Protein kinase 8 transduction kinase regulates c-Junin response to cell stress; UV irradiation of skin activates MAPK8 MMP1Matrix Proteinase/ aka Collagenase; Metalloproteinase 1 ProteinaseInhibitor cleaves collagens types I-III; plays a key role in remodelingoccuring in both normal & diseased conditions; transcriptionallyregulated by growth factors, hormones, cytokines & cellulartransformation MMP2 Matrix Proteinase/ aka Gelatinase; Metalloproteinase2 Proteinase Inhibitor cleaves collagens types IV, V, VII and gelatintype I; produced by normal skin fibroblasts; may play a role inregulation of vascularization & the inflammatory response MMP3 MatrixProteinase/ aka Stromelysin; Metalloproteinase 3 Proteinase Inhibitordegrades fibronectin, laminin, collagens III, IV, IX, X, cartilageproteoglycans, thought to be involved in wound repair; progression ofatherosclerosis & tumor initiation; produced predominantly by connectivetissue cells MMP9 Matrix Proteinase/ AKA gelatinase B; metalloproteinase9 Proteinase Inhibitor degrades extracellular matrix molecules, secretedby IL-8- stimulated neutrophils NR1I2 Nuclear transcription aka PAR2;Member receptor activation factor- of nuclear hormone subfamily 1 signaltransduction- receptor family of xenobiotic ligand-activated metabolismtranscription factors; activates transcription of cytochrome P-450 genesPCNA Proliferating DNA binding-DNA Required for both Cell Nuclearreplication-DNA DNA replication & Antigen repair-cell repair;processivity proliferation factor for DNA polymerases delta and epsilonPI3 Proteinase proteinase aka SKALP; inhibitor 3 skin inhibitor-proteinProteinase inhibitor derived binding- found in epidermis ofextracellular matrix several inflammatory skin diseases; it's expressioncan be used as a marker of skin irritancy PLAU Plasminogen Proteinase/AKA uPA; cleaves activator, Proteinase Inhibitor plasminogen tourokinase plasmin (a protease responsible for nonspecific extracellularmatrix degradation) PTGS2 Prostaglandin- Enzyme/Redox aka COX2;endoperoxide Proinflammatory, synthase 2 member of arachidonic acid toprostanoid conversion pathway; induced by proinflammatory cytokinesS100A7 S100 calcium- calcium binding- Member of S100 binding protein 7epidermal family of calcium differentiation binding proteins; localizedin the cytoplasm &/or nucleus of a wide range of cells; involved in theregulation of cell cycle progression & differentiation; markedlyoverexpressed in skin lesions of psoriatic patients TGFB1 Transformingcytokines- Pro- and growth factor, chemokines-growth antiinflammatorybeta factors activity, anti- apoptotic; cell-cell signaling, can eitherinhibit or stimulate cell growth TIMP1 Tissue Inhibitormetalloproteinase Member of TIMP of Matrix inhibitor-ECM family; naturalMetalloproteinase 1 maintenance- inhibitors of matrix positive controlcell metalloproteinases; proliferation transcriptionally induced bycytokines & hormones; mediates erythropoeisis in vitro TNF Tumornecrosis cytokines- Proinflammatory, factor, alpha chemokines-growthTH1, mediates host factors response to bacterial stimulus, regulatescell growth & differentiation TNFSF6 Tumor necrosis ligand-apoptosis akaFASL; Apoptosis factor (ligand) induction-signal antigen ligand 1 is thesuperfamily, transduction ligand for FAS; member 6 interaction of FASwith its ligand is critical in triggering apoptosis of some types ofcells such as lymphocytes; defects in protein may be related to somecases of SLE TP53 tumor protein transcription factor- Tumor protein p53,a p53 DNA binding- nuclear protein, plays tumor suppressor- a role inregulation of DNA cell cycle; binds to recombination/repair DNA p53binding site and activates expression of downstream genes that inhibitgrowth and/or invasion of tumor VEGF vascular cytokines- Producted byendothelial chemokines-growth monocytes growth factor factors

TABLE 5 Liver Metabolism and Disease Gene Expression Panel Symbol NameClassification Description ABCC1 ATP-binding cassette, Liver HealthIndicator AKA Multidrug resistance sub-family C, member 1 protein 1; AKACFTR/MRP; multispecific organic anion membrane transporter; mediatesdrug resistance by pumping xenobiotics out of cell AHR Ary1 hydrocarbonMetabolism Increases expression of receptor Receptor/Transcriptionxenobiotic metabolizing Factor enzymes (ie P450) in response to bindingof planar aromatic hydrocarbons ALB Albumin Liver Health IndicatorCarrier protein found in blood serum, synthesized in the liver,downregulation linked to decreased liver function/health COL1A1Collagen, type 1, alpha 1 Tissue Remodelling AKA Procollagen;extracellular matrix protein; implicated in fibrotic processes ofdamaged liver CYP1A1 Cytochrome P450 1A1 Metabolism Enzyme Polycyclicaromatic hydrocarbon metabolism; monooxygenase CYP1A2 Cytochrome P4501A2 Metabolism Enzyme Polycyclic aromatic hydrocarbon metabolism;monooxygenase CYP2C19 Cytochrome P450 Metabolism Enzyme Xenobioticmetabolism; 2C19 monooxygenase CYP2D6 Cytochrome P450 2D6 MetabolismEnzyme Xenobiotic metabolism; monooxygenase CYP2E Cytochrome P450 2E1Metabolism Enzyme Xenobiotic metabolism; monooxygenase; catalyzesformation of reactive intermediates from small organic molecules (i.e.ethanol, acetaminophen, carbon tetrachloride) CYP3A4 Cytochrome P450 3A4Metabolism Enzyme Xenobiotic metabolism; broad catalytic specificity,most abundantly expressed liver P450 EPHX1 Epoxide hydrolase 1,Metabolism Enzyme Catalyzes hydrolysis of microsomal reactive epoxidesto water (xenobiotic) soluble dihydrodiols FAP Fibroblast activationLiver Health Indicator Expressed in cancer stroma protein, □ and woundhealing GST Glutathione S- Metabolism Enzyme Catalyzes glutathionetransferase conjugation to metabolic substrates to form morewater-soluble, excretable compounds; primer-probe set nonspecific forall members of GST family GSTA1 and Glutathione S- Metabolism EnzymeCatalyzes glutathione A2 transferase 1A1/2 conjugation to metabolicsubstrates to form more water-soluble, excretable compounds GSTM1Glutathione S- Metabolism Enzyme Catalyzes glutathione transferase M1conjugation to metabolic substrates to form more water-soluble,excretable compounds KITLG KIT ligand Growth Factor AKA Stem cell factor(SCF); mast cell growth factor, implicated in fibrosis/cirrhosis due tochronic liver inflammation LGALS3 Lectin, galactoside- Liver HealthIndicator AKA galectin 3; Cell binding, soluble, 3 growth regulationNR1I2 Nuclear receptor Metabolism AKA Pregnane X receptor subfamily 1,group I, Receptor/Transcription (PXR); heterodimer with family 2 Factorretinoid X receptor forms nuclear transcription factor for CYP3A4 NR1I3Nuclear receptor Metabolism AKA Constitutive subfamily 1, group I,Receptor/Transcription androstane receptor beta family 3 Factor (CAR);heterodimer with retinoid X receptor forms nuclear transcription factor;mediates P450 induction by phenobarbital-like inducers. ORM1 Orosomucoid1 Liver Health Indicator AKA alpha 1 acid glycoprotein (AGP), acutephase inflammation protein PPARA Peroxisome proliferator MetabolismReceptor Binds peroxisomal activated receptor □ proliferators (ie fattyacids, hypolipidemic drugs) & controls pathway for beta- oxidation offatty acids SCYA2 Small inducible Cytokine/Chemokine AKA Monocytechemotactic cytokine A2 protein 1 (MCP1); recruits monocytes to areas ofinjury and infection, upregulated in liver inflammation UCP2 Uncouplingprotein 2 Liver Health Indicator Decouples oxidative phosphorylationfrom ATP synthesis, linked to diabetes, obesity UGT UDP- MetabolismEnzyme Catalyzes glucuronide Glucuronosyltransferase conjugation tometabolic substrates, primer-probe set nonspecific for all members ofUGT1 family

TABLE 6 Endothelial Gene Expression Panel Symbol Name ClassificationDescription ADAMTS1 Disintegrin-like and Protease AKA METH1; Inhibitsmetalloprotease endothelial cell proliferation; (reprolysin type) withmay inhibit angiogenesis; thrombospondin type 1 expression may beassociated motif, 1 with development of cancer cachexia. CLDN14 Claudin14 AKA DFNB29; Component of tight junction strands ECE1 Endothelinconverting Metalloprotease Cleaves big endothelin 1 to enzyme 1endothelin 1 EDN1 Endothelin 1 Peptide hormone AKA ET1; Endothelium-derived peptides; potent vasoconstrictor EGR1 Early growth response 1Transcription factor AKA NGF1A; Regulates the transcription of genesinvolved in mitogenesis and differentiation FLT1 Fms-related tyrosineAKA VEGFR1; FRT; kinase 1 (vascular Receptor for VEGF; involvedendothelial growth in vascular development and factor/vascularregulation of vascular permeability factor permeability receptor) GJA1gap junction protein, AKA CX43; Protein alpha 1, 43 kD component of gapjunctions; major component of gap junctions in the heart; may beimportant in synchronizing heart contractions and in embryonicdevelopment GSR Glutathione reductase 1 Oxidoreductase AKA GR; GRASE;Maintains high levels of reduced glutathione in the cytosol HIF1AHypoxia-inducible factor Transcription factor AKA MOP1; ARNT 1, alphasubunit interacting protein; mediates the transcription of oxygenregulated genes; induced by hypoxia HMOX1 Heme oxygenase Redox EnzymeAKA HO1; Essential for heme (decycling) 1 catabolism, cleaves heme toform biliverdin and CO; endotoxin inducible ICAM1 Intercellular adhesionCell Adhesion/ Endothelial cell surface molecule 1 Matrix Proteinmolecule; regulates cell adhesion and trafficking, upregulated duringcytokine stimulation IGFBP3 Insulin-like growth AKA IBP3; Expressed byfactor binding protein 3 vascular endothelial cells; may influenceinsulin-like growth factor activity IL15 Interleukin 15 cytokines-Proinflammatory; mediates T- chemokines-growth cell activation, inhibitsfactors apoptosis, synergizes with IL-2 to induce IFN-g and TNF-a IL1BInterleukin 1, beta cytokines- Proinflammatory; constitutivelychemokines-growth and inducibly expressed by factors many cell types,secreted IL8 Interleukin 8 cytokines- Proinflammatory, majorchemokines-growth secondary inflammatory factors mediator, celladhesion, signal transduction, cell-cell signaling, angiogenesis,synthesized by a wide variety of cell types MAPK1 mitogen-activatedTransferase AKA ERK2; May promote protein kinase 1 entry into the cellcycle, growth factor responsive NFKB1 Nuclear Factor kappa BTranscription Factor AKA KBF1, EBP1; Transcription factor that regulatesthe expression of infolammatory and immune genes; central role inCytokine induced expression of E- selectin NOS2A Nitric oxide synthase2A Enzyme/Redox AKA iNOS; produces NO which is bacteriocidal/tumoricidalNOS3 EndothelialNitric Oxide AKA ENOS, CNOS; Synthase Synthesizes nitricoxide from oxygen and arginine; nitric oxide is implicated in vascularsmooth muscle relaxation, vascular endothelial growth factor inducedangiogenesis, and blood clotting through the activation of plateletsPLAT Plasminogen activator, Protease AKA TPA; Converts tissueplasminogin to plasmin; involved in fibrinolysis and cell migrationPTGIS Prostaglandin I2 Isomerase AKA PGIS; PTGI; CYP8; (prostacyclin)synthase CYP8A1; Converts prostaglandin h2 to prostacyclin(vasodilator); cytochrome P450 family; imbalance of prostacyclin maycontribute to myocardial infarction, stroke, atherosclerosis PTGS2Prostaglandin- Enzyme/Redox AKA COX2; endoperoxide synthase 2Proinflammatory, member of arachidonic acid to prostanoid conversionpathway; induced by proinflammatory cytokines PTX3 pentaxin-relatedgene, AKA TSG-14; Pentaxin 3; rapidly induced by IL-1 Similar to thepentaxin beta subclass of inflammatory acute-phase proteins; novelmarker of inflammatory reactions SELE selectin E (endothelial CellAdhesion AKA ELAM; Expressed by adhesion molecule 1) cytokine-stimulatedendothelial cells; mediates adhesion of neutrophils to the vascularlining SERPINE1 Serine (or cysteine) Proteinase Inhibitor AKA PAI1;Plasminogen protease inhibitor, clade activator inhibitor type 1; B(ovalbumin), member 1 interacts with tissue plasminogen activator toregulate fibrinolysis TEK tyrosine kinase, Transferase Receptor AKATIE2, VMCM; Receptor endothelial for angiopoietin-1; may regulateendothelial cell proliferation and differentiation; involved in vascularmorphogenesis; TEK defects are associated with venous malformationsVCAM1 vascular cell adhesion Cell Adhesion/ AKA L1CAM; CD106; molecule 1Matrix Protein INCAM-100; Cell surface adhesion molecule specific forblood leukocytes and some tumor cells; mediates signal transduction; maybe linked to the development of atherosclerosis, and rheumatoidarthritis VEGF Vascular Endothelial Growth factor AKA VPF; Inducesvascular Growth Factor permeability and endothelial cell growth;associated with angiogenesis

TABLE 7 Cell Health and Apoptosis Gene Expression Panel Symbol NameClassification Description ABL1 V-abl Abelson murine leukemia oncogeneCytoplasmic and nuclear viral oncogene homolog 1 protein tyrosine kinaseimplicated in cell differentiation, division, adhesion and stressresponse. Alterations of ABL1 lead to malignant transformations. APAF1Apoptotic Protease Activating protease Cytochrome c binds to Factor 1activator APAF1, triggering activation of CASP3, leading to apoptosis.May also facilitate procaspase 9 autoactivation. BAD BCL2 Agonist ofCell Death membrane Heterodimerizes with BCLX protein and counters itsdeath repressor activity. This displaces BAX and restores itsapoptosis-inducing activity. BAK1 BCL2-antagonist/killer 1 membrane Inthe presence of an protein apropriate stimulus BAK 1 acceleratesprogramed cell death by binding to, and antagonizing the repressor BCL2or its adenovirus homolog e1b 19k protein. BAX BCL2-associated X proteinmembrane Accelerates apoptosis by protein binding to, and antagonizingBCL2 or its adenovirus homolog e1b 19k protein. It induces the releaseof cytochrome c and activation of CASP3 BCL2 B-cell CLL/lymphoma 2membrane Interferes with the activation protein of caspases bypreventing the release of cytochrome c, thus blocking apoptosis. BCL2L1BCL2-like 1 (long form) membrane Dominant regulator of protein apoptoticcell death. The long form displays cell death repressor activity,whereas the short isoform promotes apoptosis. BCL2L1 promotes cellsurvival by regulating the electrical and osmotic homeostasis ofmitochondria. BID BH3-Interacting Death Domain Induces ice-likeproteases and Agonist apoptosis. counters the protective effect of bcl-2(by similarity). Encodes a novel death agonist that heterodimerizes witheither agonists (BAX) or antagonists (BCL2). BIK BCL2-Interacting KillerAccelerates apoptosis. Binding to the apoptosis repressors BCL2L1,bhrf1, BCL2 or its adenovirus homolog e1b 19k protein suppresses thisdeath- promoting activity. BIRC2 Baculoviral IAP Repeat- apoptosis Mayinhibit apoptosis by Containing 2 suppressor regulating signals requiredfor activation of ICE-like proteases. Interacts with TRAF1 and TRAF2.Cytoplasmic BIRC3 Baculoviral IAP Repeat- apoptosis Apoptoticsuppressor. Containing 3 suppressor Interacts with TRAF1 andTRAF2.Cytoplasmic BIRC5 Survivin apoptosis Inhibits apoptosis. Inhibitorof suppressor CASP3 and CASP7. Cytoplasmic CASP1 Caspase 1 proteinaseActivates IL1B; stimulates apoptosis CASP3 Caspase 3 proteinase Involvedin activation cascade of caspases responsible for apoptosis - cleavesCASP6, CASP7, CASP9 CASP9 Caspase 9 proteinase Binds with APAF1 tobecome activated; cleaves and activates CASP3 CCNA2 Cyclin A2 cyclinDrives cell cycle at G1/S and G2/M phase; interacts with cdk2 and cdc2CCNB1 Cyclin B1 cyclin Drives cell cycle at G2/M phase; complexes withcdc2 to form mitosis promoting factor CCND1 Cyclin D1 cyclin Controlscell cycle at G1/S (start) phase; interacts with cdk4 and cdk6; hasoncogene function CCND3 Cyclin D3 cyclin Drives cell cycle at G1/Sphase; expression rises later in G1 and remains elevated in S phase;interacts with cdk4 and cdk6 CCNE1 Cyclin E1 cyclin Drives cell cycle atG1/S transition; major downstream target of CCND1; cdk2- CCNE1 activityrequired for centrosome duplication during S phase; interacts with RBcdk2 Cyclin-dependent kinase 2 kinase Associated with cyclins A, D andE; activity maximal during S phase and G2; CDK2 activation, throughcaspase- mediated cleavage of CDK inhibitors, may be instrumental in theexecution of apoptosis following caspase activation cdk4Cyclin-dependent kinase 4 kinase cdk4 and cyclin-D type complexes areresponsible for cell proliferation during G1; inhibited by CDKN2A (p16)CDKN1A Cyclin-Dependent Kinase tumor May bind to and inhibit cyclin-Inhibitor 1A (p21) suppressor dependent kinase activity, preventingphosphorylation of critical cyclin-dependent kinase substrates andblocking cell cycle progression; activated by p53; tumor suppressorfunction CDKN2B Cyclin-Dependent Kinase tumor Interacts strongly withcdk4 Inhibitor 2B (p15) suppressor and cdk6; role in growth regulationbut limited role as tumor suppressor CHEK1 Checkpoint, S. pombe Involvedin cell cycle arrest when DNA damage has occurred, or unligated DNA ispresent; prevents activation of the cdc2-cyclin b complex DAD1 DefenderAgainst Cell Death membrane Loss of DAD1 protein triggers proteinapoptosis DFFB DNA Fragmentation Factor, 40-KD, nuclease Induces DNAfragmentation Beta Subunit and chromatin condensation during apoptosis;can be activated by CASP3 FADD Fas (TNFRSF6)-associated via co-receptorApoptotic adaptor molecule death domain that recruits caspase-8 orcaspase-10 to the activated fas (cd95) or tnfr-1 receptors; thisdeath-inducing signalling complex performs CASP8 proteolytic activationGADD45A Growth arrest and DNA damage regulator of Stimulates DNAexcision inducible, alpha DNA repair repair in vitro and inhibits entryof cells into S phase; binds PCNA K-ALPHA-1 Alpha Tubulin, ubiquitousmicrotubule Major constituent of peptide microtubules; binds 2 moleculesof GTP MADD MAP-kinase activating death co-receptor Associates withTNFR1 domain through a death domain-death domain interaction;Overexpression of MADD activates the MAP kinase ERK2, and expression ofthe MADD death domain stimulates both the ERK2 and JNK1 MAP kinases andinduces the phosphorylation of cytosolic phospholipase A2 MAP3K14Mitogen-activated protein kinase Activator of NFKB1 kinase kinase kinase14 MRE11A Meiotic recombination (S. cerevisiae) nuclease Exonucleaseinvolved in DNA 11 homolog A double-strand breaks repair NFKB1 Nuclearfactor of kappa light nuclear p105 is the precursor of the polypeptidegene enhancer in B- translational p50 subunit of the nuclear cells 1(p105) regulator factor NFKB, which binds to the kappa-b consensussequence located in the enhancer region of genes involved in immuneresponse and acute phase reactions; the precursor does not bind DNAitself PDCD8 Programmed Cell Death 8 enzyme, The principal mitochondrial(apoptosis-inducing factor) reductase factor causing nuclear apoptosis.Independent of caspase apoptosis. PNKP Polynucleotide kinase 3′-phosphatase Catalyzes the 5-prime phosphatase phosphorylation of nucleicacids and can have associated 3-prime phosphatase activity, predictiveof an important function in DNA repair following ionizing radiation oroxidative damage PTEN Phosphatase and tensin homolog tumor Tumorsuppressor that (mutated in multiple advanced suppressor modulates G1cell cycle cancers 1) progression through negatively regulating thePI3-kinase/Akt signaling pathway; one critical target of this signalingprocess is the cyclin-dependent kinase inhibitor p27 (CDKN1B). RAD52RAD52 (S. cerevisiae) homolog DNA binding Involved in DNA double-proteinsor stranded break repair and meiotic/mitotic recombination RB1Retinoblastoma 1 (including tumor Regulator of cell growth;osteosarcoma) suppressor interacts with E2F-like transcription factor; anuclear phosphoprotein with DNA binding activity; interacts with histonedeacetylase to repress transcription SMAC Second mitochondria-derivedmitochondrial Promotes caspase activation in activator of caspasepeptide cytochrome c/APAF-1/ caspase 9 pathway of apoptosis TERTTelomerase reverse transcriptase transcriptase Ribonucleoprotein whichin vitro recognizes a single- stranded G-rich telomere primer and addsmultiple telomeric repeats to its 3-prime end by using an RNA templateTNF Tumor necrosis factor cytokines- Proinflammatory, TH1, chemokines-mediates host response to growth factors bacterial stimulus, regulatescell growth & differentiation TNFRSF11A Tumor necrosis factor receptorreceptor Activates NFKB1; Important superfamily, member 11a, regulatorof interactions activator of NFKB between T cells and dendritic cellsTNFRSF12 Tumor necrosis factor receptor receptor Induces apoptosis andactivates superfamily, member 12 NF-kappaB; contains a (translocatingchain-association cytoplasmic death domain and membrane protein)transmembrane domains TOSO Regulator of Fas-induced receptor Potentinhibitor of Fas induced apoptosis apoptosis; expression of TOSO, likethat of FAS and FASL, increases after T-cell activation, followed by adecline and susceptibility to apoptosis; hematopoietic cells expressingTOSO resist anti- FAS-, FADD-, and TNF- induced apoptosis withoutincreasing expression of the inhibitors of apoptosis BCL2 and BCLXL;cells expressing TOSO and activated by FAS have reduced CASP8 andincreased CFLAR expression, which inhibits CASP8 processing TP53 TumorProtein 53 DNA binding Activates expression of genes protein-cell thatinhibit tumor growth cycle-tumor and/or invasion; involved in suppressorcell cycle regulation (required for growth arrest at G1); inhibits cellgrowth through activation of cell-cycle arrest and apoptosis TRADDTNFRSF1A-associated via co-receptor Overexpression of TRADD death domainleads to 2 major TNF-induced responses, apoptosis and activation ofNF-kappa-B TRAF1 TNF receptor-associated factor 1 co-receptor Interactwith cytoplasmic domain of TNFR2 TRAF2 TNF receptor-associated factor 2co-receptor Interact with cytoplasmic domain of TNFR2 VDAC1Voltage-dependent anion membrane Functions as a voltage-gated channel 1protein pore of the outer mitochondrial membrane; proapoptotic proteinsBAX and BAK accelerate the opening of VDAC allowing cytochrome c toenter, whereas the antiapoptotic protein BCL2L1 closes VDAC by bindingdirectly to it XRCC5 X-ray repair complementing helicase Functionstogether with the defective repair in Chinese DNA ligase IV-XRCC4hamster cells 5 complex in the repair of DNA double-strand breaks

TABLE 8 Cytokine Gene Expression Panel Symbol Name ClassificationDescription CSF3 Colony Stimulating Cytokines/ AKA G-CSF; Cytokine thatFactor 3 (Granulocyte) Cytokines/Growth stimulates granulocyte Factorsdevelopment IFNG Interferon, Gamma Cytokines/ Pro- and anti-inflammatoryChemokines/Growth activity; TH1 cytokine; factors nonspecificinflammatory mediator; produced by activated T-cells. Antiproliferativeeffects on transformed cells. IL1A Interleukin 1, Alpha Cytokines/Proinflammatory; Chemokines/Growth constitutively and inducibly factorsexpressed in variety of cells Generally cytosolic and released onlyduring severe inflammatory disease IL1B Interleukin 1, Beta Cytokines/Proinflammatory; constitutively Chemokines/Growth and induciblyexpressed factors by many cell types, secreted IL1RN Interleukin 1,Receptor Cytokines/ IL1 receptor antagonist; AntagonistChemokines/Growth Antiinflammatory; inhibits factors binding of IL-1 toIL-1 receptor by binding to receptor without stimulating IL-1-likeactivity IL2 Interleukin 2 Cytokines/ T-cell growth factor, expressedChemokines/Growth by activated T-cells, regulates factors lymphocyteactivation and differentiation; inhibits apoptosis, TH1 cytokine IL4Interleukin 4 Cytokines/ Antiinflammatory; TH₂; Chemokines/Growthsuppresses proinflammatory factors cytokines, increases expression ofIL-1RN, regulates lymphocyte activation IL5 Interleukin 5 Cytokines/Eosinophil stimulatory factor; Chemokines/Growth stimulates late B cellfactors differentiation to secretion of Ig IL6 Interleukin 6 Cytokines/AKA Interferon, Beta 2; Pro- Chemokines/Growth and anti-inflammatoryactivity, factors TH₂ cytokine, regulates hematopoiesis, activation ofinnate response, osteoclast development; elevated in sera of patientswith metastatic cancer IL10 Interleukin 10 Cytokines/ Antiinflammatory;TH₂; Chemokines/Growth Suppresses production of factors proinflammatorycytokines IL12\\BROMMAIN\VOL1\ALL Interleukin 12 (p40) Cytokines/Proinflammatory; mediator of Primer Probe Tech Chemokines/Growth innateimmunity, TH₁ Sheets\Completed\IL!@B factors cytokine, requires co-tksht.doc stimulation with IL-18 to induce IFN-γ IL13 Interleukin 13Cytokines/ Inhibits inflammatory cytokine Chemokines/Growth productionfactors IL15 Interleukin 15 Cytokines/ Proinflammatory; mediates T-Chemokines/Growth cell activation, inhibits factors apoptosis,synergizes with IL- 2 to induce IFN-γ and TNF-α IL18 Interleukin 18Cytokines/ Proinflammatory, TH1, innate Chemokines/Growth and aquiredimmunity, factors promotes apoptosis, requires co-stimulation with IL-1or IL-2 to induce TH1 cytokines T- and NK-cells IL18BP IL-18 BindingProtein Cytokines/ Implicated in inhibition of Chemokines/Growth earlyTH1 cytokine responses factors TGFA Transforming GrowthTransferase/Signal Proinflammatory cytokine that Factor, AlphaTransduction is the primary mediator of immune response and regulation,Associated with TH1 responses, mediates host response to bacterialstimuli, regulates cell growth & differentiation; Negative regulation ofinsulin action TGFB1 Transforming Growth Cytokines/ AKA DPD1, CED; Pro-and Factor, Beta 1 Chemokines/Growth antiinflammatory activity factorsAnti-apoptotic; cell-cell signaling, Can either inhibit or stimulatecell growth; Regulated by glucose in NIDDM individuals, overexpression(due to oxidative stress promotes renal cell hypertrophy leading todiabetic nephropathy TNFSF5 Tumor Necrosis Factor Cytokines/ Ligand forCD40; Expressed (Ligand) Superfamily, Chemokines/Growth on the surfaceof T-cells; Member 5 factors Regulates B-cell function by engaging CD40on the B-cell surface TNFSF6 Tumor Necrosis Factor Cytokines/ AKA FASL;Apoptosis (Ligand) Superfamily, Chemokines/Growth antigen ligand 1 isthe ligand Member 6 factors for FAS antigen; Critical in triggeringapoptosis of some types of cells such as lymphocytes; Defects in proteinmay be related to cases of SLE TNFSF13B Tumor Necrosis Factor Cytokines/B-cell activating factor, TNF (Ligand) Superfamily, Chemokines/Growthfamily Member 13B factors

TABLE 9 TNF/IL1 Inhibition Gene Expression Panel HUGO Symbol NameClassification Description CD14 CD14 Cell Marker LPS receptor used asmarker for Antigen monocytes GRO1 GRO1 Cytokines/Chemokines/ AKA SCYB1,Melanoma Oncogene Growth factors growth stimulating activity, Alpha;Chemotactic for neutrophils HMOX1 Heme Enzyme: Redox Enzyme that cleavesheme to Oxygenase form biliverdin and CO; (Decycling) 1 Endotoxininducible ICAM1 Intercellular Cell Adhesion: Matrix Endothelial cellsurface Adhesion Protein molecule; Regulates cell Molecule 1 adhesionand trafficking; Up- regulated during cytokine stimulation IL1BInterleukin 1, Cytokines/Chemokines/ Pro-inflammatory; Beta Growthfactors Constitutively and inducibly expressed by many cell types;Secreted IL1RN Interleukin 1 Cytokines/Chemokines/ Anti-inflammatory;Inhibits Receptor Growth factors binding of IL-1 to IL-1 receptorAntagonist by binding to receptor without stimulating IL-1-like activityIL10 Interleukin 10 Cytokines/Chemokines/ Anti-inflammatory; TH₂ Growthfactors cytokine; Suppresses production of pro-inflammatory cytokinesMMP9 Matrix Proteinase/Proteinase AKA Gelatinase B; DegradesMetalloproteinase 9 Inhibitor extracellular matrix molecules; Secretedby IL-8 stimulated neutrophils SERPINE1 Serine (or Proteinase/ProteinaseAKA Plasminogen activator Cysteine) Inhibitor inhibitor-1, PAI-1;Regulator of Protease fibrinolysis Inhibitor, Clade E (Ovalbumin),Member 1 TGFB1 Transforming Cytokines/Chemokines/ Pro- andanti-inflammatory Growth Growth factors activity; Anti-apoptotic;Cell-cell Factor, Beta 1 signaling; Can either inhibit or stimulate cellgrowth TIMP1 Tissue Proteinase/Proteinase Irreversibly binds andinhibits Inhibitor of Inhibitor metalloproteinases such asMetalloproteinase 1 collagenase TNFA Tumor Cytokines/Chemokines/Pro-inflammatory; TH₁ cytokine; Necrosis Growth factors Mediates hostresponse to Factor, Alpha bacterial stimulus; Regulates cell growth &differentiation

TABLE 10 Chemokine Gene Expression Panel Symbol Name ClassificationDescription CCR1 chemokine (C-C Chemokine receptor A member of the betamotif) receptor 1 chemokine receptor family (seven transmembraneprotein). Binds SCYA3/MIP- 1a, SCYA5/RANTES, MCP-3, HCC-1, 2, and 4, andMPIF-1. Plays role in dendritic cell migration to inflammation sites andrecruitment of monocytes. CCR3 chemokine (C-C Chemokine receptor C-Ctype chemokine receptor motif) receptor 3 (Eotaxin receptor) binds toEotaxin, Eotaxin-3, MCP-3, MCP-4, SCYA5/RANTES and mip-1 delta therebymediating intracellular calcium flux. Alternative co-receptor with CD4for HIV-1 infection. Involved in recruitment of eosinophils. Primarily aTh2 cell chemokine receptor. CCR5 chemokine (C-C Chemokine receptorMember of the beta chemokine motif) receptor 5 receptor family (seventransmembrane protein). Binds to SCYA3/MIP-1a and SCYA5/RANTES.Expressed by T cells and macrophages, and is an important co-receptorfor macrophage-tropic virus, including HIV, to enter host cells. Plays arole in Th1 cell migration. Defective alleles of this gene have beenassociated with the HIV infection resistance. CX3CR1 chemokine (C—X3—C)Chemokine receptor CX3CR1 is an HIV coreceptor receptor 1 as well as aleukocyte chemotactic/adhesion receptor for fractalkine. Natural killercells predominantly express CX3CR1 and respond to fractalkine in bothmigration and adhesion. CXCR4 chemokine (C—X—C Chemokine receptorReceptor for the CXC motif), receptor 4 chemokine SDF1. Acts as a(fusin) co-receptor with CD4 for lymphocyte-tropic HIV-1 viruses. Playsrole in B cell, Th2 cell and naive T cell migration. GPR9 Gprotein-coupled Chemokine receptor CXC chemokine receptor receptor 9binds to SCYB10/IP-10, SCYB9/MIG, SCYB11/I- TAC. Binding of chemokinesto GPR9 results in integrin activation, cytoskeletal changes andchemotactic migration. Prominently expressed in in vitro culturedeffector/memory T cells and plays a role in Th1 cell migration. GRO1GRO1 oncogene Chemokine AKA SCYB1; chemotactic for (melanoma growthneutrophils. GRO1 is also a stimulating activity, mitogenic polypeptidesecreted alpha) by human melanoma cells. GRO2 GRO2 oncogene ChemokineAKA MIP2, SCYB2; (MIP-2) Macrophage inflammatory protein produced bymoncytes and neutrophils. Belongs to intercrine family alpha (CXCchemokine). IL8 interleukin 8 Chemokine Proinflammatory, major secondaryinflammatory mediator, cell adhesion, signal transduction, cell-cellsignaling, angiogenesis, synthesized by a wide variety of cell types PF4Platelet Factor 4 Chemokine PF4 is released during platelet (SCYB4)aggregation and is chemotactic for neutrophils and monocytes. PF4'smajor physiologic role appears to be neutralization of heparin-likemolecules on the endothelial surface of blood vessels, therebyinhibiting local antithrombin III activity and promoting coagulation.SCYA2 small inducible Chemokine Recruits monocytes to areas of cytokineA2 (MCP1) injury and infection. Stimulates IL-4 production; implicatedin diseases involving monocyte, basophil infiltration of tissue (ie.g.,psoriasis, rheumatoid arthritis, atherosclerosis). SCYA3 small inducibleChemokine A “monokine” involved in the cytokine A3 (MIP1a) acuteinflammatory state through the recruitment and activation ofpolymorphonuclear leukocytes. A major HIV- suppressive factor producedby CD8-positive T cells. SCYA5 small inducible Chemokine Binds to CCR1,CCR3, and cytokine A5 CCR5 and is a chemoattractant (RANTES) for bloodmonocytes, memory t helper cells and eosinophils. A majorHIV-suppressive factor produced by CD8- positive T cells. SCYB10 smallinducible Chemokine A CXC subfamily chemokine. cytokine subfamily BBinding of SCYB10 to (Cys-X-Cys), receptor CXCR3/GPR9 results member 10in stimulation of monocytes, natural killer and T-cell migration, andmodulation of adhesion molecule expression. SCYB10 is Induced by IFNgand may be a key mediator in IFNg response. SDF1 stromal cell-derivedChemokine Belongs to the CXC subfamily factor 1 of the intercrinefamily, which activate leukocytes. SDF1 is the primary ligand for CXCR4,a coreceptor with CD4 for human immunodeficiency virus type 1 (HIV-1).SDF1 is a highly efficacious lymphocyte chemoattractant.

TABLE 11 Breast Cancer Gene Expression Panel Symbol Name ClassificationDescription ACTB Actin, beta Cell Structure Actins are highly conservedproteins that are involved in cell motility, structure and integrity.ACTB is one of two non-muscle cytoskeletal actins. Site of action forcytochalasin B effects on cell motility. BCL2 B-cell membrane proteinInterferes with the activation of CLL/lymphoma 2 caspases by preventingthe release of cytochrome c, thus blocking apoptosis. CD19 CD19 antigenCell Marker AKA Leu 12; B cell growth factor CD34 CD34 antigen CellMarker AKA: hematopoietic progenitor cell antigen. Cell surface antigenselectively expressed on human hematopoietic progenitor cells.Endothelial marker. CD44 CD44 antigen Cell Marker Cell surface receptorfor hyaluronate. Probably involved in matrix adhesion, lymphocyteactivation and lymph node homing. DC13 DC13 protein unknown functionDSG1 Desmoglein 1 membrane protein Calcium-binding transmembraneglycoprotein involved in the interaction of plaque proteins andintermediate filaments mediating cell-cell adhesion. Interact withcadherins. EDR2 Early The specific function in human Development cellshas not yet been determined. Regulator 2 May be part of a complex thatmay regulate transcription during embryonic development. ERBB2 v-erb-b2Oncogene Oncogene. Overexpression of erythroblastic ERBB2 confers Taxolresistance leukemia viral in breast cancers. Belongs to the oncogene EGFtyrosine kinase receptor homolog 2 family. Binds gp130 subunit of theIL6 receptor in an IL6 dependent manner. An essential component of IL-6signalling through the MAP kinase pathway. ERBB3 v-erb-b2 OncogeneOncogene. Overexpressed in Erythroblastic mammary tumors. Belongs to theLeukemia Viral EGF tyrosine kinase receptor Oncogene family. Activatedthrough Homolog 3 neuregulin and ntak binding. ESR1 Estrogen Receptor/ESR1 is a ligand-activated Receptor 1 Transcription Factor transcriptionfactor composed of several domains important for hormone binding, DNAbinding, and activation of transcription. FGF18 Fibroblast Growth FactorInvolved in a variety of biological Growth Factor 18 processes,including embryonic development, cell growth, morphogenesis, tissuerepair, tumor growth, and invasion. FLT1 Fms-related Receptor Receptorfor VEGF; involved in tyrosine kinase 1 vascular development andregulation of vascular permeability. FOS V-fos FBJ murine Oncogene/Leucine zipper protein that forms osteosarcoma Transcriptional thetranscription factor AP-1 by viral oncogene Activator dimerizing withJUN. Implicated homolog in the processes of cell proliferation,differentiation, transformation, and apoptosis. GRO1 GRO1 oncogeneChemokine/Growth Proinflammatory; chemotactic for Factor/Oncogeneneutrophils. Growth regulator that modulates the expression ofmetalloproteinase activity. IFNG Interferon, Cytokine Pro- andantiinflammatory gamma activity; TH1 cytokine; nonspecific inflammatorymediator; produced by activated T-cells. Antiproliferative effects ontransformed cells. IRF5 Interferon Transcription Factor Regulatestranscription of regulatory factor 5 interferon genes through DNAsequence-specific binding. Diverse roles, include virus- mediatedactivation of interferon, and modulation of cell growth,differentiation, apoptosis, and immune system activity. KRT14 Keratin 14Cytoskeleton Type I keratin, intermediate filament component; KRT14 isdetected in the basal layer, with lower expression in more apicallayers, and is not present in the stratum corneum. Together with KRT5forms the cytoskeleton of epithelial cells. KRT19 Keratin 19Cytoskeleton Type I epidermal keratin; may form intermediate filaments.Expressed often in epithelial cells in culture and in some carcinomasKRT5 Keratin 5 Cytoskeleton Coexpressed with KRT14 to form cytoskeletonof epithelial cells. KRT5 expression is a hallmark of mitotically activekeratinocytes and is the primary structural component of the 10 nmintermediate filaments of the mitotic epidermal basal cells. MDM2 Mdm2,Oncogene/ Inhibits p53- and p73-mediated transformed 3T3 TranscriptionFactor cell cycle arrest and apoptosis by cell double binding itstranscriptional minute 2, p53 activation domain, resulting in bindingprotein tumorigenesis. Permits the nuclear export of p53 and targets itfor proteasome-mediated proteolysis. MMP9 Matrix Proteinase/ Degradesextracellular matrix by metalloproteinase 9 Proteinase Inhibitorcleaving types IV and V collagen. Implicated in arthritis andmetastasis. MP1 Metalloprotease 1 Proteinase/ Member of the pitrilysinfamily. Proteinase Inhibitor A metalloendoprotease. Could play a broadrole in general cellular regulation. N33 Putative prostate TumorSuppressor Integral membrane protein. cancer tumor Associated withhomozygous suppressor deletion in metastatic prostate cancer. OXCT3-oxoacid CoA Transferase OXCT catalyzes the reversible transferasetransfer of coenzyme A from succinyl-CoA to acetoacetate as the firststep of ketolysis (ketone body utilization) in extrahepatic tissues.PCTK1 PCTAIRE protein Belongs to the SER/THR family of kinase 1 proteinkinases; CDC2/CDKX subfamily. May play a role in signal transductioncascades in terminally differentiated cells. SERPINB5 Serine proteinaseProteinase/ Protease Inhibitor; Tumor inhibitor, clade B, ProteinaseInhibitor/ suppressor, especially for member 5 Tumor Suppressormetastasis. Inhibits tumor invasion by inhibiting cell motility. SRP19Signal Responsible for signal- recognition recognition-particleassembly. particle 19 kD SRP mediates the targeting of proteins to theendoplasmic reticulum. STAT1 Signal transducer DNA-Binding Binds to theIFN-Stimulated and activator of Protein Response Element (ISRE) and totranscription 1, the GAS element; specifically 91 kD required forinterferon signaling. STAT1 can be activated by IFN- alpha, IFN-gamma,EGF, PDGF and IL6. BRCA1-regulated genes overexpressed in breasttumorigenesis included STAT1 and JAK1. TGFB3 Transforming CellSignalling Transmits signals through growth factor, transmembraneserine/threonine beta 3 kinases. Increased expression of TGFB3 maycontribute to the growth of tumors. TLX3 T-cell leukemia, TranscriptionFactor Member of the homeodomain homeobox 3 family of DNA bindingproteins. May be activated in T-ALL leukomogenesis. VWF Von WillebrandCoagulation Factor Multimeric plasma glycoprotein factor active in theblood coagulation system as an antihemophilic factor (VIIIC) carrier andplatelet-vessel wall mediator. Secreted by endothelial cells.

TABLE 12 Infectious Disease Gene Expression Panel Symbol NameClassification Description C1QA Complement Proteinase/ Serum complementsystem; forms C1 component 1, q Proteinase complex with the proenzymesc1r and subcomponent, alpha Inhibitor c1s polypeptide CASP1 Caspase 1proteinase Activates IL1B; stimulates apoptosis CD14 CD14 antigen CellMarker LPS receptor used as marker for monocytes CSF2 Granulocyte-cytokines- AKA GM-CSF; Hematopoietic monocyte colony chemokines- growthfactor; stimulates growth and stimulating factor growth factorsdifferentiation of hematopoietic precursor cells from various lineages,including granulocytes, macrophages, eosinophils, and erythrocytes EGR1Early growth cell signaling master inflammatory switch for response-1and activation ischemia-related responses including chemokine sysntheis,adhesion moelcules and macrophage differentiation F3 F3 Enzyme/ AKAthromboplastin, Coagulation Redox Factor 3; cell surface glycoproteinresponsible for coagulation catalysis GRO2 GRO2 oncogene cytokines- AKAMIP2, SCYB2; Macrophage chemokines- inflammatory protein produced bygrowth factors moncytes and neutrophils HMOX1 Heme oxygenase Enzyme/Endotoxin inducible (decycling) 1 Redox HSPA1A Heat shock protein 70Cell Signaling heat shock protein 70 kDa and activation ICAM1Intercellular adhesion Cell Adhesion/ Endothelial cell surface molecule;molecule 1 Matrix Protein regulates cell adhesion and trafficking,upregulated during cytokine stimulation IFI16 gamma interferon cellsignaling Transcriptional repressor inducible protein 16 and activationIFNG Interferon gamma cytokines- Pro- and antiinflammatory activity,chemokines- TH1 cytokine, nonspecific growth factors inflammatorymediator, produced by activated T-cells IL10 Interleukin 10 cytokines-Antiinflammatory; TH2; suppresses chemokines- production ofproinflammatory growth factors cytokines IL12B Interleukin 12 p40cytokines- Proinflammatory; mediator of innate chemokines- immunity, TH1cytokine, requires co- growth factors stimulation with IL-18 to induceIFN-g IL13 Interleukin 13 cytokines- Inhibits inflammatory cytokinechemokines- production growth factors IL18 Interleukin 18 cytokines-Proinflammatory, TH1, innate and chemokines- aquired immunity, promotesgrowth factors apoptosis, requires co-stimulation with IL-1 or IL-2 toinduce TH1 cytokines in T- and NK-cells IL18BP IL-18 Binding cytokines-Implicated in inhibition of early TH1 Protein chemokines- cytokineresponses growth factors IL1A Interleukin 1, alpha cytokines-Proinflammatory; constitutively and chemokines- inducibly expressed invariety of cells. growth factors Generally cytosolic and released onlyduring severe inflammatory disease IL1B Interleukin 1, beta cytokines-Proinflammatory; constitutively and chemokines- inducibly expressed bymany cell growth factors types, secreted IL1R1 interleukin 1 receptorAKA: CD12 or IL1R1RA receptor, type I IL1RN Interleukin 1 receptorcytokines- IL1 receptor antagonist; antagonist chemokines-Antiinflammatory; inhibits binding of growth factors IL-1 to IL-1receptor by binding to receptor without stimulating IL-1-like activityIL2 Interleukin 2 cytokines- T-cell growth factor, expressed bychemokines- activated T-cells, regulates growth factors lymphocyteactivation and differentiation; inhibits apoptosis, TH1 cytokine IL4Interleukin 4 cytokines- Antiinflammatory; TH2; suppresses chemokines-proinflammatory cytokines, increases growth factors expression ofIL-1RN, regulates lymphocyte activation IL6 Interleukin 6 cytokines-Pro- and antiinflammatory activity, (interferon, beta 2) chemokines- TH2cytokine, regulates growth factors hemotopoietic system and activationof innate response IL8 Interleukin 8 cytokines- Proinflammatory, majorsecondary chemokines- inflammatory mediator, cell adhesion, growthfactors signal transduction, cell-cell signaling, angiogenesis,synthesized by a wide variety of cell types MMP3 Matrix Proteinase/ AKAstromelysin; degrades metalloproteinase 3 Proteinase fibronectin,laminin and gelatin Inhibitor MMP9 Matrix Proteinase/ AKA gelatinase B;degrades metalloproteinase 9 Proteinase extracellular matrix molecules,Inhibitor secreted by IL-8-stimulated neutrophils PLA2G7 PhospholipaseA2, Enzyme/ Platelet activating factor group VII (platelet Redoxactivating factor acetylhydrolase, plasma) PLAU Plasminogen Proteinase/AKA uPA; cleaves plasminogen to activator, urokinase Proteinase plasmin(a protease responsible for Inhibitor nonspecific extracellular matrixdegradation) SERPINE1 Serine (or cysteine) Proteinase/ Plasminogenactivator inhibitor-1/ protease inhibitor, Proteinase PAI-1 clade B(ovalbumin), Inhibitor member 1 SOD2 superoxide dismutase OxidoreductaseEnzyme that scavenges and destroys 2, mitochondrial free radicals withinmitochondria TACI Tumor necrosis factor cytokines- T cell activatingfactor and calcium receptor superfamily, chemokines- cyclophilinmodulator member 13b growth factors TIMP1 tissue inhibitor ofProteinase/ Irreversibly binds and inhibits metalloproteinase 1Proteinase metalloproteinases, such as Inhibitor collagenase TLR2toll-like receptor 2 cell signaling mediator of petidoglycan and andactivation lipotechoic acid induced signalling TLR4 toll-like receptor 4cell signaling mediator of LPS induced signalling and activation TNFTumor necrosis cytokines- Proinflammatory, TH1, mediates host factor,alpha chemokines- response to bacterial stimulus, growth factorsregulates cell growth & differentiation TNFSF13B Tumor necrosis factorcytokines- B cell activating factor, TNF family (ligand) superfamily,chemokines- member 13b growth factors TNFSF5 Tumor necrosis factorcytokines- ligand for CD40; expressed on the (ligand) superfamily,chemokines- surface of T cells. It regulates B cell member 5 growthfactors function by engaging CD40 on the B cell surface TNFSF6 Tumornecrosis factor cytokines- AKA FasL; Ligand for FAS antigen; (ligand)superfamily, chemokines- transduces apoptotic signals into cells member6 growth factors VEGF vascular endothelial cytokines- Producted bymonocytes growth factor chemokines- growth factors IL5 Interleukin 5Cytokines- Eosinophil stimulatory factor; chemokines- stimulates late Bcell differentiation to growth factors secretion of Ig IFNA2 Interferonalpha 2 Cytokines- interferon produced by macrophages chemokines- withantiviral effects growth factors TREM1 TREM-1 Triggering Receptor/CellSignaling and Receptor Activation Expressed on Myeloid Cells 1 SCYB10small inducible Chemokine A CXC subfamily chemokine. cytokine subfamilyB Binding of SCYB10 to receptor (Cys-X-Cys), CXCR3/GPR9 results instimulation member 10 of monocytes, natural killer and T-cell migration,and modulation of adhesion molecule expression. SCYB10 is Induced byIFNg and may be a key mediator in IFNg response. CCR1 Chemokine (C-CChemokine A member of the beta chemokine motif) receptor 1 receptorreceptor family (seven transmembrane protein). Binds SCYA3/MIP-1a,SCYA5/RANTES, MCP-3, HCC-1, 2, and 4, and MPIF-1. Plays role indendritic cell migration to inflammation sites and recruitment ofmonocytes. CCR3 Chemokine (C-C Chemokine C-C type chemokine receptormotif) receptor 3 receptor (Eotaxin receptor) binds to Eotaxin,Eotaxin-3, MCP-3, MCP-4, SCYA5/RANTES and mip-1 delta thereby mediatingintracellular calcium flux. Alternative co-receptor with CD4 for HIV-1infection. Involved in recruitment of eosinophils. Primarily a Th2 cellchemokine receptor. SCYA3 Small inducile Chemokine A “monokine” involvedin the acute cytokine A3 (MIP1a) inflammatory state through therecruitment and activation of polymorphonuclear leukocytes. A majorHIV-suppressive factor produced by CD8-positive T cells. CX3CR1Chemokine (C—X3—C) Chemokine CX3CR1 is an HIV coreceptor as receptor 1receptor well as a leukocyte chemotactic/adhesion receptor forfractalkine. Natural killer cells predominantly express CX3CR1 andrespond to fractalkine in both migration and adhesion. 2331/00134.395672v1

1. A method of predicting change in a biological condition of a subjectas a result of exposure to an agent, the method comprising: a) producinga first index for a sample from the subject in the absence of the agent,the sample providing a source of RNAs comprising: i) using amplificationfor quantitatively measuring the amount of RNA of at least twoconstituents from any one of Tables 1 through 12 from the sample fromthe subject, wherein a panel of constituents is selected so thatmeasurement conditions of the constituents enables evaluation of saidbiological condition and wherein the measures of all constituents in thepanel form a first profile data set, wherein said amplification for eachconstituent is under conditions that are (1) within a degree ofrepeatability of better than five percent; and (2) the efficiencies ofamplification are within two percent; ii) using amplification forquantitatively measuring the amount of RNA of all constituents in saidpanel wherein the measures of all constituents in the panel are from arelevant population of subjects and form a normative baseline profiledata set; and iii) applying values from the first profile data set to anindex function derived using latent class modeling, thereby providing asingle-valued measure of the biological condition so as to produce anindex pertinent to the biological condition of the subject; wherein theindex function also uses data from the normative baseline profile dataset, b) producing a second index for a second sample from the subject inthe presence of the agent, the second index based on the same panel ofconstituents; and c) using the first and second indices to predictchange in the biological condition of the subject as a result ofexposure to the agent.
 2. A method according to claim 1, wherein theagent is a compound.
 3. A method according to claim 2, wherein thecompound is therapeutic.